Academic literature on the topic 'Multilayer Perceptron neural network model'
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Journal articles on the topic "Multilayer Perceptron neural network model"
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Madhiarasan, M., Mohamed Louzazni, and Partha Pratim Roy. "Novel Cooperative Multi-Input Multilayer Perceptron Neural Network Performance Analysis with Application of Solar Irradiance Forecasting." International Journal of Photoenergy 2021 (October 27, 2021): 1–24. http://dx.doi.org/10.1155/2021/7238293.
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To forecast solar irradiance with higher accuracy and generalization capability is challenging in the photovoltaic (PV) energy system. Meteorological parameters are highly influential in solar irradiance, leading to intermittent and randomicity. Forecasting using a single neural network model does not have sufficient generalization ability to achieve the optimal forecasting of solar irradiance. This paper proposes a novel cooperative multi-input multilayer perceptron neural network (CMMLPNN) to mitigate the issues related to generalization and meteorological effects. Authors develop a proposed forecasting neural network model based on the amalgamation of two inputs, three inputs, four inputs, five inputs, and six inputs associated multilayer perceptron neural network. In the proposed forecasting model (CMMLPNN), the authors overcome the variance based on the meteorological parameters. The amalgamation of five multi-input multilayer perceptron neural networks leads to better generalization ability. Some individual multilayer perceptron neural network-based forecasting models outperform in some situations, but cannot assure generalization ability and suffer from the meteorological weather condition. The proposed CMMLPNN (cooperative multi-input multilayer perceptron neural network) achieves better forecasting accuracy with the generalization ability. Therefore, the proposed forecasting model is superior to other neural network-based forecasting models and existing models.
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Rudenko, Oleg, Oleksandr Bezsonov, and Oleksandr Romanyk. "Neural network time series prediction based on multilayer perceptron." Development Management 17, no. 1 (2019): 23–34. http://dx.doi.org/10.21511/dm.5(1).2019.03.
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Until recently, the statistical approach was the main technique in solving the prediction problem. In the framework of static models, the tasks of forecasting, the identification of hidden periodicity in data, analysis of dependencies, risk assessment in decision making, and others are solved. The general disadvantage of statistical models is the complexity of choosing the type of the model and selecting its parameters. Computing intelligence methods, among which artificial neural networks should be considered at first, can serve as alternative to statistical methods. The ability of the neural network to comprehensively process information follows from their ability to generalize and isolate hidden dependencies between input and output data. Significant advantage of neural networks is that they are capable of learning and generalizing the accumulated knowledge. The article proposes a method of neural networks training in solving the problem of prediction of the time series. Most of the predictive tasks of the time series are characterized by high levels of nonlinearity and non-stationary, noisiness, irregular trends, jumps, abnormal emissions. In these conditions, rigid statistical assumptions about the properties of the time series often limit the possibilities of classical forecasting methods. The alternative methods to statistical methods can be the methods of computational intelligence, which include artificial neural networks. The simulation results confirmed that the proposed method of training the neural network can significantly improve the prediction accuracy of the time series.
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Pukach, A. I., and V. M. Teslyuk. "SUBJECTIVE PERCEPTION MODEL OF SOFTWARE COMPLEXES SUPPORT OBJECT WITH THE ENCAPSULATION OF A MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS." Ukrainian Journal of Information Technology 6, no. 2 (2024): 1–10. https://doi.org/10.23939/ujit2024.02.001.
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The object of research in this article – is the process of subjective perception of supported software complexes or their support processes by relevant human entities directly or indirectly interacting with these supported software complexes. Subjective perception model of the software complexes support object with the possibility of encapsulation of artificial neural networks, in particular – a multilayer perceptron, has been developed. Developed model provides possibility to perform modelling of the subjective perception processes of support objects (both the supported software complex itself and the processes of its support) – as one of the important scientific and applied tasks in the direction of scientific and applied problem of software complexes support automation. The developed model general concept provides possibility of artificial neural networks (of all existing types) encapsulation inside the model. In particular, this article considers the encapsulation of the multilayer perceptron type artificial neural network as an example. This paper also considers the main requirements and questions regarding the correspondence, correctness and completeness of the encapsulated multilayer perceptron artificial neural network into the developed model of subjective perception. The developed model is a universal tool that provides possibility to interpret the subjective perceptions of any researchable objects (not only software complexes), and the provided possibility of artificial neural networks encapsulation ensures the possibility of using all the advantages of artificial intelligence, including: increasing the level of automation and intellectualization of modelling process, as well as providing the opportunity for its learning. The result of model development – is a clearly structured and formalized (within the framework of the developed model, presented in this article) process (and the result of this process) of the subjective perception of researched object – the supported software complex, or its support processes. The developed model of subjective perception provides possibilities for resolving a lot of applied practical problems, among which, as an example, this work demonstrates usage of the developed model to solve the practical problem of creating the averaged (general) portrait of the software complex support team.
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Serhiienko, A. V., and E. A. Kolomoichenko. "Study of handwritten character recognition algorithms for different languages using the KAN Neural Network Model." Reporter of the Priazovskyi State Technical University. Section: Technical sciences 1, no. 49 (2024): 36–47. https://doi.org/10.31498/2225-6733.49.1.2024.321184.
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The paper analyzed the most effective existing methods of optical character recognition that use deep learning neural networks in their structure. The analysis revealed that modern neural network architectures with the best recognition accuracy indicators have a constant accuracy limit. It was also found that each analyzed neural network architecture contains a multilayer perceptron in its structure. To optimize the recognition performance of neural networks, it was proposed to use the Kolmogorov-Arnold network as an alternative to multilayer perceptron based networks. The architecture of the created model is based on a two-component transformer, the first component is a visual transformer used as an encoder, the second is a language transformer used as a decoder. The Kolmogorov-Arnold network replaces the feedforward network based on a multilayer perceptron, in each transformer – encoder and decoder. Improvement of existing neural network results is ensured through transfer learning, for which group rational functions are used as the main learning elements of the Kolmogorov-Arnold network. The model was trained on sets of images of text lines from three different writing systems: alphabetic, abugida and logographic; which are represented by the scripts: English, Devanagari and Chinese. As a result of experimental studies, high character recognition rates were found for the Chinese and Devanagari data sets but low for the English script, for the model with the Kolmogorov-Arnold network. The obtained results indicate new possibilities for increasing the reliability and efficiency of modern handwriting recognition systems
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Al-Hroot, Yusuf Ali. "A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis." International Business Research 9, no. 12 (2016): 121. http://dx.doi.org/10.5539/ibr.v9n12p121.
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<p>The main purpose of this study is to develop and compare the classification accuracy of bankruptcy prediction models using the multilayer perceptron neural network, and discriminant analysis, for the industrial sector in Jordan. The models were developed using the ten popular financial ratios found to be useful in earlier studies and expected to predict bankruptcy. The study sample was divided into two samples; the original sample (n=14) for developing the two models and a hold-out sample (n=18) for testing the prediction of models for three years prior to bankruptcy during the period from 2000 to 2014.</p><p>The results indicated that there was a difference in prediction accuracy between models in two and three years prior to failure. The results indicated that the multilayer perceptron neural network model achieved a higher overall classification accuracy rate for all three years prior to bankruptcy than the discriminant analysis model. Furthermore, the prediction rate was 94.44% two years prior to bankruptcy using multilayer perceptron neural network model and 72.22% using the discriminant analysis model. This is a significant difference of 22.22%. On the other side, the prediction rate of 83.34% three years prior to bankruptcy using multilayer perceptron neural network model and 61.11% using discriminant analysis model. We indicate there was a difference exists of 22.23%. In addition, the multilayer perceptron neural network model provides in the first two years prior to bankruptcy the lowest percentage of type I error.</p>
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Li, Yong, Qidan Zhu, and Ahsan Elahi. "Quadcopter Trajectory Tracking Based on Model Predictive Path Integral Control and Neural Network." Drones 9, no. 1 (2024): 9. https://doi.org/10.3390/drones9010009.
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This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Predictive Path Integral controller. The Model Predictive Path Integral controller uses the model to control the quadcopter following the desired trajectory. Experimental data show that the improved Model Predictive Path Integral–Multilayer Perceptron method reduces the trajectory tracking error by 10.3%, 9.8%, and 5.7% compared to the traditional Model Predictive Path Integral, MPC with MLP, and a two-layer network, respectively. These results demonstrate the potential application of the method in complex environments.
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Khan, Mohd Jawad Ur Rehman, and Anjali Awasthi. "Machine learning model development for predicting road transport GHG emissions in Canada." WSB Journal of Business and Finance 53, no. 2 (2019): 55–72. http://dx.doi.org/10.2478/wsbjbf-2019-0022.
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Abstract Prediction of greenhouse gas (GHG) emissions is important to minimise their negative impact on climate change and global warming. In this article, we propose new models based on data mining and supervised machine learning algorithms (regression and classification) for predicting GHG emissions arising from passenger and freight road transport in Canada. Four models are investigated, namely, artificial neural network multilayer perceptron, multiple linear regression, multinomial logistic regression and decision tree models. From the results, it was found that artificial neural network multilayer perceptron model showed better predictive performance over other models. Ensemble technique (Bagging & Boosting) was applied on the developed multilayer perceptron model, which significantly improved the model’s predictive performance.
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Maca, Petr, and Pavel Pech. "Forecasting SPEI and SPI Drought Indices Using the Integrated Artificial Neural Networks." Computational Intelligence and Neuroscience 2016 (2016): 1–17. http://dx.doi.org/10.1155/2016/3868519.
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The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were derived for the period of 1948–2002 on two US catchments. The meteorological and hydrological data were obtained from MOPEX experiment. The training of both neural network models was made by the adaptive version of differential evolution, JADE. The comparison of models was based on six model performance measures. The results of drought indices forecast, explained by the values of four model performance indices, show that the integrated neural network model was superior to the feedforward multilayer perceptron with one hidden layer of neurons.
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Akbar Maulana and Enny Itje Sela. "The Implementation of Artificial Neural Networks for Stock Price Prediction." Journal of Engineering, Electrical and Informatics 3, no. 3 (2023): 34–44. http://dx.doi.org/10.55606/jeei.v3i3.2254.
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This research is based on a problem that is difficult to predict stock prices, especially for beginners. Stock prices are hard to predict because they are fluctuating. Users will be easier to predict stock prices through artificial neural networks using Multilayer Perceptron. This MLP is a variant of an artificial neural network and is a development of perceptron. The selection of the Multilayer Perceptron method is based on the ability to solve various problems both classification and regression. The research conducted by the author is a regression problem as the MLP is tasked to predict the close price or closing price of stock after seven days. The results of the model built are able to predict stock prices and produce good accuracy because the resulting RMSE value produced 0.042649862994352014, which is close to 0.
 
 Keywords: Machine Learning, Stock Price Prediction, Neural Network, Multilayer Perceptron, MLP.
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Buevich, A. G., I. E. Subbotina, A. V. Shichkin, A. P. Sergeev, and E. M. Baglaeva. "Prediction of the chrome distribution in subarctic Noyabrsk using co-kriging, generalized regression neural network, multilayer perceptron, and hybrid technics." Геоэкология. Инженерная геология. Гидрогеология. Геокриология, no. 2 (May 18, 2019): 77–86. http://dx.doi.org/10.31857/s0869-78092019277-86.
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Combination of geostatistical interpolation (kriging) and machine learning (artificial neural networks, ANN) methods leads to an increase in the accuracy of forecasting. The paper considers the application of residual kriging of an artificial neural network to predicting the spatial contamination of the surface soil layer with chromium (Cr). We reviewed and compared two neural networks: the generalized regression neural network (GRNN) and multilayer perceptron (MLP), as well as the combined method: multilayer perceptron residual kriging (MLPRK). The study is based on the results of the screening of the surface soil layer in the subarctic Noyabrsk, Russia. The models are developed based on computer modeling with minimization of the RMSE. The MLPRK model showed the best prognostic accuracy.
Dissertations / Theses on the topic "Multilayer Perceptron neural network model"
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Goosen, Johannes Christiaan. "Comparing generalized additive neural networks with multilayer perceptrons / Johannes Christiaan Goosen." Thesis, North-West University, 2011. http://hdl.handle.net/10394/5552.
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In this dissertation, generalized additive neural networks (GANNs) and multilayer perceptrons (MLPs) are studied
and compared as prediction techniques. MLPs are the most widely used type of artificial neural network
(ANN), but are considered black boxes with regard to interpretability. There is currently no simple a priori
method to determine the number of hidden neurons in each of the hidden layers of ANNs. Guidelines exist that
are either heuristic or based on simulations that are derived from limited experiments. A modified version of
the neural network construction with cross–validation samples (N2C2S) algorithm is therefore implemented and
utilized to construct good MLP models. This algorithm enables the comparison with GANN models. GANNs
are a relatively new type of ANN, based on the generalized additive model. The architecture of a GANN is less
complex compared to MLPs and results can be interpreted with a graphical method, called the partial residual
plot. A GANN consists of an input layer where each of the input nodes has its own MLP with one hidden layer.
Originally, GANNs were constructed by interpreting partial residual plots. This method is time consuming and
subjective, which may lead to the creation of suboptimal models. Consequently, an automated construction
algorithm for GANNs was created and implemented in the SAS R
statistical language. This system was called
AutoGANN and is used to create good GANN models.
A number of experiments are conducted on five publicly available data sets to gain insight into the similarities
and differences between GANN and MLP models. The data sets include regression and classification tasks.
In–sample model selection with the SBC model selection criterion and out–of–sample model selection with the
average validation error as model selection criterion are performed. The models created are compared in terms
of predictive accuracy, model complexity, comprehensibility, ease of construction and utility.
The results show that the choice of model is highly dependent on the problem, as no single model always
outperforms the other in terms of predictive accuracy. GANNs may be suggested for problems where interpretability
of the results is important. The time taken to construct good MLP models by the modified N2C2S
algorithm may be shorter than the time to build good GANN models by the automated construction algorithm<br>Thesis (M.Sc. (Computer Science))--North-West University, Potchefstroom Campus, 2011.
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Wilgenbus, Erich Feodor. "The file fragment classification problem : a combined neural network and linear programming discriminant model approach / Erich Feodor Wilgenbus." Thesis, North-West University, 2013. http://hdl.handle.net/10394/10215.
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The increased use of digital media to store legal, as well as illegal data, has created the need
for specialized tools that can monitor, control and even recover this data. An important task
in computer forensics and security is to identify the true le type to which a computer le
or computer le fragment belongs. File type identi cation is traditionally done by means
of metadata, such as le extensions and le header and footer signatures. As a result,
traditional metadata-based le object type identi cation techniques work well in cases where
the required metadata is available and unaltered. However, traditional approaches are not
reliable when the integrity of metadata is not guaranteed or metadata is unavailable. As
an alternative, any pattern in the content of a le object can be used to determine the
associated le type. This is called content-based le object type identi cation.
Supervised learning techniques can be used to infer a le object type classi er by exploiting
some unique pattern that underlies a le type's common le structure. This study builds
on existing literature regarding the use of supervised learning techniques for content-based
le object type identi cation, and explores the combined use of multilayer perceptron neural
network classi ers and linear programming-based discriminant classi ers as a solution to the
multiple class le fragment type identi cation problem.
The purpose of this study was to investigate and compare the use of a single multilayer
perceptron neural network classi er, a single linear programming-based discriminant classi-
er and a combined ensemble of these classi ers in the eld of le type identi cation. The
ability of each individual classi er and the ensemble of these classi ers to accurately predict
the le type to which a le fragment belongs were tested empirically.
The study found that both a multilayer perceptron neural network and a linear programming-
based discriminant classi er (used in a round robin) seemed to perform well in solving
the multiple class le fragment type identi cation problem. The results of combining
multilayer perceptron neural network classi ers and linear programming-based discriminant
classi ers in an ensemble were not better than those of the single optimized classi ers.<br>MSc (Computer Science), North-West University, Potchefstroom Campus, 2013
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Ridhagen, Markus, and Petter Lind. "A comparative study of Neural Network Forecasting models on the M4 competition data." Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445568.
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The development of machine learning research has provided statistical innovations and further developments within the field of time series analysis. This study seeks to investigate two different approaches on artificial neural network models based on different learning techniques, and answering how well the neural network approach compares with a basic autoregressive approach, as well as how the artificial neural network models compare to each other. The models were compared and analyzed in regards to the univariate forecast accuracy on 20 randomly drawn time series from two different time frequencies from the M4 competition dataset. Forecasting was made dependent on one time lag (t-1) and forecasted three and six steps ahead respectively. The artificial neural network models outperformed the baseline Autoregressive model, showing notably lower mean average percentage error overall. The Multilayered perceptron models performed better than the Long short-term memory model overall, whereas the Long short-term memory model showed improvement on longer prediction time dimensions. As the training were done univariately on a limited set of time steps, it is believed that the one layered-approach gave a good enough approximation on the data, whereas the added layer couldn’t fully utilize its strengths of processing power. Likewise, the Long short-term memory model couldn’t fully demonstrate the advantagements of recurrent learning. Using the same dataset, further studies could be made with another approach to data processing. Implementing an unsupervised approach of clustering the data before analysis, the same models could be tested with multivariate analysis on models trained on multiple time series simultaneously.
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Gao, Zhenning. "Parallel and Distributed Implementation of A Multilayer Perceptron Neural Network on A Wireless Sensor Network." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1383764269.
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Midhall, Ruben, and Amir Parmbäck. "Utvärdering av Multilayer Perceptron modeller för underlagsdetektering." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43469.
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Antalet enheter som är uppkopplade till internet, Internet of Things (IoT), ökar hela tiden. År 2035 beräknas det finnas 1000 miljarder Internet of Things-enheter. Samtidigt som antalet enheter ökar, ökar belastningen på internet-nätverken som enheterna är uppkopplade till. Internet of Things-enheterna som finns i vår omgivning samlar in data som beskriver den fysiska tillvaron och skickas till molnet för beräkning. För att hantera belastningen på internet-nätverket flyttas beräkningarna på datan till IoT-enheten, istället för att skicka datan till molnet. Detta kallas för edge computing. IoT-enheter är ofta resurssnåla enheter med begränsad beräkningskapacitet. Detta innebär att när man designar exempelvis "machine learning"-modeller som ska köras med edge computing måste algoritmerna anpassas utifrån de resurser som finns tillgängliga på enheten. I det här arbetet har vi utvärderat olika multilayer perceptron-modeller för mikrokontrollers utifrån en rad olika experiment. "Machine learning"-modellerna har varit designade att detektera vägunderlag. Målet har varit att identifiera hur olika parametrar påverkar "machine learning"-systemen. Vi har försökt att maximera prestandan och minimera den mängd fysiskt minne som krävs av modellerna. Vi har även behövt förhålla oss till att mikrokontrollern inte haft tillgång till internet. Modellerna har varit ämnade att köras på en mikrokontroller "on the edge". Datainsamlingen skedde med hjälp av en accelerometer integrerad i en mikrokontroller som monterades på en cykel. I studien utvärderas två olika "machine learning"-system, ett som är en kombination av binära klassificerings modeller och ett multiklass klassificerings system som framtogs i ett tidigare arbete. Huvudfokus i arbetet har varit att träna modeller för klassificering av vägunderlag och sedan utvärdera modellerna. Datainsamlingen gjordes med en mikrokontroller utrustad med en accelerometer monterad på en cykel. Ett av systemen lyckas uppnå en träffsäkerhet på 93,1\% för klassificering av 3 vägunderlag. Arbetet undersöker även hur mycket fysiskt minne som krävs av de olika "machine learning"-systemen. Systemen krävde mellan 1,78kB och 5,71kB i fysiskt minne.<br>The number of devices connected to the internet, the Internet of Things (IoT), is constantly increasing. By 2035, it is estimated to be 1,000 billion Internet of Things devices in the world. At the same time as the number of devices increase, the load on the internet networks to which the devices are connected, increases. The Internet of Things devices in our environment collect data that describes our physical environment and is sent to the cloud for computation. To reduce the load on the internet networks, the calculations are done on the IoT devices themselves instead of in the cloud. This way no data needs to be sent over the internet and is called edge computing. In edge computing, however, other challenges arise. IoT devices are often resource-efficient devices with limited computing capacity. This means that when designing, for example, machine learning models that are to be run with edge computing, the models must be designed based on the resources available on the device. In this work, we have evaluated different multilayer perceptron models for microcontrollers based on a number of different experiments. The machine learning models have been designed to detect road surfaces. The goal has been to identify how different parameters affect the machine learning systems. We have tried to maximize the performance and minimize the memory allocation of the models. The models have been designed to run on a microcontroller on the edge. The data was collected using an accelerometer integrated in a microcontroller mounted on a bicycle. The study evaluates two different machine learning systems that were developed in a previous thesis. The main focus of the work has been to create algorithms for detecting road surfaces. The data collection was done with a microcontroller equipped with an accelerometer mounted on a bicycle. One of the systems succeeds in achieving an accuracy of 93.1\% for the classification of 3 road surfaces. The work also evaluates how much physical memory is required by the various machine learning systems. The systems required between 1.78kB and 5,71kB of physical memory.
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Albarakati, Noor. "FAST NEURAL NETWORK ALGORITHM FOR SOLVING CLASSIFICATION TASKS." VCU Scholars Compass, 2012. http://scholarscompass.vcu.edu/etd/2740.
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Classification is one-out-of several applications in the neural network (NN) world. Multilayer perceptron (MLP) is the common neural network architecture which is used for classification tasks. It is famous for its error back propagation (EBP) algorithm, which opened the new way for solving classification problems given a set of empirical data. In the thesis, we performed experiments by using three different NN structures in order to find the best MLP neural network structure for performing the nonlinear classification of multiclass data sets. A developed learning algorithm used here is the batch EBP algorithm which uses all the data as a single batch while updating the NN weights. The batch EBP speeds up training significantly and this is also why the title of the thesis is dubbed 'fast NN …'. In the batch EBP, and when in the output layer a linear neurons are used, one implements the pseudo-inverse algorithm to calculate the output layer weights. In this way one always finds the local minimum of a cost function for a given hidden layer weights. Three different MLP neural network structures have been investigated while solving classification problems having K classes: one model/K output layer neurons, K separate models/One output layer neuron, and K joint models/One output layer neuron. The extensive series of experiments performed within the thesis proved that the best structure for solving multiclass classification problems is a K joint models/One output layer neuron structure.
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Birkmire, Brian Michael. "Weapon Engagement Zone Maximum Launch Range Approximation using a Multilayer Perceptron." Wright State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=wright1313763379.
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Ignatavičienė, Ieva. "Tiesioginio sklidimo neuroninių tinklų sistemų lyginamoji analizė." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120801_133809-03141.
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Pagrindinis darbo tikslas – atlikti kelių tiesioginio sklidimo neuroninių tinklų sistemų lyginamąją analizę siekiant įvertinti jų funkcionalumą.
Šiame darbe apžvelgiama: biologinio ir dirbtinio neuronų modeliai, neuroninių tinklų klasifikacija pagal jungimo konstrukciją (tiesioginio sklidimo ir rekurentiniai neuroniniai tinklai), dirbtinių neuroninių tinklų mokymo strategijos (mokymas su mokytoju, mokymas be mokytojo, hibridinis mokymas). Analizuojami pagrindiniai tiesioginio sklidimo neuroninių tinklų metodai: vienasluoksnis perceptronas, daugiasluoksnis perceptronas realizuotas „klaidos skleidimo atgal” algoritmu, radialinių bazinių funkcijų neuroninis tinklas.
Buvo nagrinėjama 14 skirtingų tiesioginio sklidimo neuroninių tinklų sistemos. Programos buvo suklasifikuotos pagal kainą, tiesioginio sklidimo neuroninių tinklo mokymo metodų taikymą, galimybę vartotojui keisti parametrus prieš apmokant tinklą ir techninį programos įvertinimą. Programos buvo įvertintos dešimtbalėje vertinimo sistemoje pagal mokymo metodų įvairumą, parametrų keitimo galimybes, programos stabilumą, kokybę, bei kainos ir kokybės santykį. Aukščiausiu balu įvertinta „Matlab” programa (10 balų), o prasčiausiai – „Sharky NN” (2 balai).
Detalesnei analizei pasirinktos keturios programos („Matlab“, „DTREG“, „PathFinder“, „Cortex“), kurios buvo įvertintos aukščiausiais balais, galėjo apmokyti tiesioginio sklidimo neuroninį tinklą daugiasluoksnio perceptrono metodu ir bent dvi radialinių bazinių funkcijų... [toliau žr. visą tekstą]<br>The main aim – to perform a comparative analysis of several feedforward neural system networks in order to identify its functionality.
The work presents both: biological and artificial neural models, also classification of neural networks, according to connections’ construction (of feedforward and recurrent neural networks), studying strategies of artificial neural networks (with a trainer, without a trainer, hybrid). The main methods of feedforward neural networks: one-layer perceptron, multilayer perceptron, implemented upon “error feedback” algorithm, also a neural network of radial base functions have been considered.
The work has included 14 different feedforward neural system networks, classified according its price, application of study methods of feedforward neural networks, also a customer’s possibility to change parameters before paying for the network and a technical evaluation of a program. The programs have been evaluated from 1 point to 10 points according to the following: variety of training systems, possibility to change parameters, stability, quality and ratio of price and quality. The highest evaluation has been awarded to “Matlab” (10 points), the lowest – to “Sharky NN” (2 points).
Four programs (”Matlab“, “DTREG“, “PathFinder“,”Cortex“) have been selected for a detail analysis. The best evaluated programs have been able to train feedforward neural networks using multilayer perceptron method, also at least two radial base function networks. “Matlab“ and... [to full text]
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Steinholtz, Tim. "Skip connection in a MLP network for Parkinson’s classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-303130.
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In this thesis, two different architecture designs of a Multi-Layer Perceptron network have been implemented. One architecture being an ordinary MLP, and in the other adding DenseNet inspired skip connections to an MLP architecture. The models were used and evaluated on the classification task, where the goal was to classify if subjects were diagnosed with Parkinson’s disease or not based on vocal features. The models were trained on an openly available dataset for Parkinson’s classification and evaluated on a hold-out set from this dataset and on two datasets recorded in another sound recording environment than the training data. The thesis searched for the answer to two questions; How insensitive models for Parkinson’s classification are to the sound recording environment and how the proposed skip connections in an MLP model could help improve performance and generalization capacity. The thesis results show that the sound environment affects the accuracy. Nevertheless, it concludes that one would be able to overcome this with more time and allow for good accuracy when models are exposed to data from a new sound environment than the training data. As for the question, if the skip connections improve accuracy and generalization, the thesis cannot draw any broad conclusions due to the data that were used. The models had, in general, the best performance with shallow networks, and it is with deeper networks that the skip connections are argued to help improve these attributes. However, when evaluating on the data from a different sound recording environment than the training data, the skip connections had the best performance in two out of three tests.<br>I denna avhandling har två olika arkitektur designer för ett artificiellt flerskikts neuralt nätverk implementerats. En arkitektur som följer konventionen för ett vanlig MLP nätverk, samt en ny arkitektur som introducerar DenseNet inspirerade genvägs kopplingar i MLP nätverk. Modellerna användes och utvärderades för klassificering, vars mål var att urskilja försökspersoner som friska eller diagnostiserade med Parkinsons sjukdom baserat på röst attribut. Modellerna tränades på ett öppet tillgänglig dataset för Parkinsons klassificering och utvärderades på en delmängd av denna data som inte hade använts för träningen, samt två dataset som kommer från en annan ljudinspelnings miljö än datan för träningen. Avhandlingen sökte efter svaret på två frågor; Hur okänsliga modeller för Parkinsons klassificering är för ljudinspelnings miljön och hur de föreslagna genvägs kopplingarna i en MLP-modell kan bidra till att förbättra prestanda och generalisering kapacitet. Resultaten av avhandlingen visar att ljudmiljön påverkar noggrannheten, men drar slutsatsen att med mer tid skulle man troligen kunna övervinna detta och möjliggöra god noggrannhet i nya ljudmiljöer. När det kommer till om genvägs kopplingarna förbättrar noggrannhet och generalisering, är avhandlingen inte i stånd att dra några breda slutsatser på grund av den data som användes. Modellerna hade generellt bästa prestanda med grunda nätverk, och det är i djupare nätverk som genvägs kopplingarna argumenteras för att förbättra dessa egenskaper. Med det sagt, om man bara kollade på resultaten på datan som är ifrån en annan ljudinspelnings miljö så hade genvägs arkitekturen bättre resultat i två av de tre testerna som utfördes.
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Tamas, Wani Théo. "Prévision statistique de la qualité de l’air et d’épisodes de pollution atmosphérique en Corse." Thesis, Corte, 2015. http://www.theses.fr/2015CORT0010/document.
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L’objectif de ces travaux de doctorat est de développer un modèle prédictif capable de prévoir correctement les concentrations en polluants du jour pour le lendemain en Corse. Nous nous sommes intéressés aux PM10 et à l’ozone, les deux polluants les plus problématiques sur l’île. Le modèle devait correspondre aux contraintes d’un usage opérationnel au sein d’une petite structure, comme Qualitair Corse, l’association locale de surveillance de la qualité de l’air.La prévision a été réalisée à l’aide de réseaux de neurones artificiels. Ces modèles statistiques offrent une grande précision tout en nécessitant peu de ressources informatiques. Nous avons choisi le Perceptron MultiCouche (PMC), avec en entrée à la fois des mesures de polluants, des mesures météorologiques, et des sorties de modèles de chimie-transport (CHIMERE via la plate-forme AIRES) et de modèles météorologiques (AROME).La configuration des PMC a été optimisée avant leur apprentissage automatique, en conformité avec le principe de parcimonie. Pour en améliorer les performances, une étude de sélection de variables a été au préalable menée. Nous avons comparé l’usage d’algorithmes génétiques, de recuits simulés et d’analyse en composantes principales afin d’optimiser le choix des variables d’entrées. L’élagage du PMC a été également mis en œuvre.Nous avons ensuite proposé un nouveau type de modèle hybride, combinaison d’un classifieur et de plusieurs PMC, chacun spécialisé sur un régime météorologique particulier. Ces modèles, qui demandent un large historique de données d’apprentissage, permettent d’améliorer la prévision des valeurs extrêmes et rares, correspondant aux pics de pollution. La classification non-supervisée a été menée avec des cartes auto-organisatrices couplées à l’algorithme des k-means, ainsi que par classification hiérarchique ascendante. L’analyse de sensibilité à été menée grâce à l’usage de courbes ROC.Afin de gérer les jeux de données utilisés, de mener les expérimentations de manière rigoureuse et de créer les modèles destinés à l’usage opérationnel, nous avons développé l’application « Aria Base », fonctionnant sous Matlab à l’aide de la Neural Network Toolbox.Nous avons également développé l’application « Aria Web » destinée à l’usage quotidien à Qualitair Corse. Elle est capable de mener automatiquement les prévisions par PMC et de synthétiser les différentes informations qui aident la prévision rendues disponibles sur internet par d’autres organismes<br>The objective of this doctoral work is to develop a forecasting model able to correctly predict next day pollutant concentrations in Corsica. We focused on PM10 and ozone, the two most problematic pollutants in the island. The model had to correspond to the constraints of an operational use in a small structure like Qualitair Corse, the local air quality monitoring association.The prediction was performed using artificial neural networks. These statistical models offer a great precision while requiring few computing resources. We chose the MultiLayer Perceptron (MLP), with input data coming from pollutants measurements, meteorological measurements, chemical transport model (CHIMERE via AIRES platform) and numerical weather prediction model (AROME).The configuration of the MLP was optimized prior to machine learning, in accordance with the principle of parsimony. To improve forecasting performances, we led a feature selection study. We compared the use of genetic algorithms, simulated annealing and principal component analysis to optimize the choice of input variables. The pruning of the MLP was also implemented.Then we proposed a new type of hybrid model, combination of a classification model and various MLPs, each specialized on a specific weather pattern. These models, which need large learning datasets, allow an improvement of the forecasting for extreme and rare values, corresponding to pollution peaks. We led unsupervised classification with self organizing maps coupled with k-means algorithm, and with hierarchical ascendant classification. Sensitivity analysis was led with ROC curves.We developed the application “Aria Base” running with Matlab and its Neural Network Toolbox, able to manage our datasets, to lead rigorously the experiments and to create operational models.We also developed the application “Aria Web” to be used daily by Qualitair Corse. It is able to lead automatically the prevision with MLP, and to synthesize forecasting information provided by other organizations and available on the Internet
Books on the topic "Multilayer Perceptron neural network model"
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Anderson, James A. Brain Theory. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.003.0012.
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What form would a brain theory take? Would it be short and punchy, like Maxwell’s Equations? Or with a clear goal but achieved by a community of mechanisms—local theories—to attain that goal, like the US Tax Code. The best developed recent brain-like model is the “neural network.” In the late 1950s Rosenblatt’s Perceptron and many variants proposed a brain-inspired associative network. Problems with the first generation of neural networks—limited capacity, opaque learning, and inaccuracy—have been largely overcome. In 2016, a program from Google, AlphaGo, based on a neural net using deep learning, defeated the world’s best Go player. The climax of this chapter is a fictional example starring Sherlock Holmes demonstrating that complex associative computation in practice has less in common with accurate pattern recognition and more with abstract high-level conceptual inference.
Book chapters on the topic "Multilayer Perceptron neural network model"
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Hoya, Tetsuya. "Review of the Two Existing Artificial Neural Network Models—Multilayer Perceptron and Probabilistic Neural Networks." In Syntactic Networks—Kernel Memory Approach. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-57312-5_2.
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Thinh, Le Vinh, Nguyen Le Van Thanh, Tran Thien Huan, and Nguyen Thanh Nha. "Human Gait Classification Model Based on Data of IMU Sensor and Multilayer Perceptron Neural Network Model." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99666-6_121.
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El-Hassani, Fatima Zahrae, Youssef Ghanou, and Khalid Haddouch. "A Novel Model for Optimizing Multilayer Perceptron Neural Network Architecture Based on Genetic Algorithm Method." In Artificial Intelligence and Industrial Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43520-1_31.
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Sreedevi, B., Durga Karthik, J. Glory Thephoral, M. Jeya Pandian, and G. Revathy. "A Novel Neural Network Based Model for Diabetes Prediction Using Multilayer Perceptron and Jrip Classifier." In Pervasive Computing and Social Networking. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2840-6_27.
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Ghaleb, Sanaa A. A., Mumtazimah Mohamad, Engku Fadzli Hasan Syed Abdullah, and Waheed A. H. M. Ghanem. "An Integrated Model to Email Spam Classification Using an Enhanced Grasshopper Optimization Algorithm to Train a Multilayer Perceptron Neural Network." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6835-4_27.
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Sykes, Edward R., Jinhe Zhang, and Uri Sevilla. "Assisting Personal Support Worker’s e-Training with AI Prediction." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-90341-0_13.
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Abstract The increasing need for effective caregiver training, particularly for Personal Support Workers, has led to the development of innovative e-training platforms. This study explores the application of advanced ML models to predict training outcomes and identify at-risk learners early in the process. The primary goal is to improve training completion rates while ensuring compliance with industry standards. We employed a range of ML models, including Decision Trees, Random Forest, Support Vector Machines, Neural Networks, to predict the likelihood of successful course completion using a dataset comprising over 27 million user interaction records. Feature engineering was used to extract key metrics such as module and lesson completion ratios. The results indicate that the Multilayer Perceptron model performed best, achieving an AUC score of 0.99, while K-NN also demonstrated strong performance with an AUC of 0.98. Key features such as module completion ratio and temporal progress were found to be significant predictors of training success. These findings suggest that integrating predictive analytics into e-training platforms can significantly enhance the effectiveness of PSW certification processes, ultimately supporting the growing demand for skilled caregivers in healthcare.
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Gaci, Said. "A NOVEL MODEL TO ESTIMATE S-WAVE VELOCITY INTEGRATING HÖLDERIAN REGULARITY, EMPIRICAL MODE DECOMPOSITION, AND MULTILAYER PERCEPTRON NEURAL NETWORKS." In Oil and Gas Exploration. John Wiley & Sons, Inc., 2017. http://dx.doi.org/10.1002/9781119227519.ch12.
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Weick, M. "Hopfield Model, Boltzmann Machine, Multilayer Perceptron and Selected Applications." In Neural and Synergetic Computers. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74119-7_8.
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Hao, Jianbin, Shaohua Tan, and Joos Vandewalle. "A Geometric Approach to the Structural Synthesis of Multilayer Perceptron Neural Networks." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_120.
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Woo, Dong-Min, and Dong-Chul Park. "Application of MultiLayer Perceptron Type Neural Network to Camera Calibration." In Advances in Intelligent and Soft Computing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_15.
Full textConference papers on the topic "Multilayer Perceptron neural network model"
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López Flores, Walter Jeremías. "Evaluation of Neural Network and Logit Models for Classification of Default in Banking Loans." In I Conferencia Internacional de Ciencia, Tecnología e Innovación. Trans Tech Publications Ltd, 2024. http://dx.doi.org/10.4028/p-dxrv7c.
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The purpose of the study was to evaluate the performance of neural networks as modern techniques to classify the risk of default against the traditional Logit statistical method, taking a Honduran bank as a case study. The data was obtained from its credit portfolio made up of 38,156 personal loans and 9 available characteristics, choosing the most representative independent variables to design a Multilayer Perceptron type base model and its Logit equivalent to which characteristics were added to analyze their impact on the classification of the dependent variable Default, leaving in the end a network with an input layer of 8 nodes, 4 hidden dense layers of 20 and 24 nodes, a central dropout layer and a node in the output layer as well as an equivalent logistic regression to compare the performance of both. The results with unbalanced data showed a superior performance of the networks, but when applying SMOTE oversampling, although there was no greater impact on the network, there was in the regressions, concluding that these learn to classify loan default better when the data subsets are balanced in the class of the response variable since its new results almost reached those of the neural network, which was finally chosen as the preferred model for its implementation with an accuracy of 99.16%, precision of 99.47%, sensitivity of 99.59%, specificity of 95.48 %, F1 score of 99.53% and ROC and PR curves with AUC of 98.68% and 97.69% respectively.
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Pupezescu, Valentin. "PULSATING MULTILAYER PERCEPTRON." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-035.
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The Knowledge Discovery in Databases represents the process of extracting useful information from data that are stored in real databases. The Knowledge Discovery in Databases process consists of multiple steps which include selection target data from raw data, preprocessing, data transformation, Data Mining and interpretation of mined data. As we see, the Data Mining is one step from the whole process and it will perform one of these Data Mining task: classification, regression, clustering, association rules, summarization, dependency modelling, change and deviation detection. In this experiments I used one neural network(multilayer perceptron) that performs the classification task. This paper proposes a functioning model for the classical multilayer perceptron that is a sequential simulation of a Distributed Committee Machine. Committee Machines are a group of neural structures that work in a distributed manner as a group in order to obtain better classification results than individual neural networks. The classical backpropagation algorithm is modified in order to simulate the execution of multiple multilayer perceptrons that run in a sequential manner. The classification was made for three standard data sets: iris1, wine1 and conc1. In my case the backpropagation algorithm still consists of three well known stages: the feedforward of the input training pattern, the calculation of the associated output error, and the correction of the weights. The proposed model makes a twist for the classical backpropagation algorithm meaning that all the weights of the multilayer perceptron will be reset and randomly regenerated after a certain number of training epochs. This model will have a pulsating effect that will also prevent the blockage of the perceptron on poor local minimum points. This research is useful in the Knowledge Discovery in Databases process because the classification gets the same performance results as in the case of a Distributed Committee Machine.
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Saromo, Daniel, Elizabeth Villota, and Edwin Villanueva. "Auto-Rotating Perceptrons." In LatinX in AI at Neural Information Processing Systems Conference 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai2019120826.
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This paper proposes an improved design of the perceptron unit to mitigate the vanishing gradient problem. This nuisance appears when training deep multilayer perceptron networks with bounded activation functions. The new neuron design, named auto-rotating perceptron (ARP), has a mechanism to ensure that the node always operates in the dynamic region of the activation function, by avoiding saturation of the perceptron. The proposed method does not change the inference structure learned at each neuron. We test the effect of using ARP units in some network architectures which use the sigmoid activation function. The results support our hypothesis that neural networks with ARP units can achieve better learning performance than equivalent models with classic perceptrons.
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Ghorbanian, Kaveh, and Mohammad Gholamrezaei. "Axial Compressor Performance Map Prediction Using Artificial Neural Network." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27165.
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The application of artificial neural network to compressor performance map prediction is investigated. Different types of artificial neural network such as multilayer perceptron network, radial basis function network, general regression neural network, and a rotated general regression neural network proposed by the authors are considered. Two different models are utilized in simulating the performance map. The results indicate that while the rotated general regression neural network has the least mean error and best agreement to the experimental data, it is however limited to curve fitting application. On the other hand, if one considers a tool for curve fitting as well as for interpolation and extrapolation applications, multilayer perceptron network technique is the most powerful candidate. Further, the compressor efficiency based on the multilayer perceptron network technique is determined. Excellent agreement between the predictions and the experimental data is obtained.
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Xu, Dongxin, Dao Wen Chen, Qian Ma, Bo Xu, and Taiyi Huang. "Adaptation of neural network model: comparison of multilayer perceptron and LVQ." In 3rd International Conference on Spoken Language Processing (ICSLP 1994). ISCA, 1994. http://dx.doi.org/10.21437/icslp.1994-406.
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Xiao-Wei, Wang. "A Multilayer Perceptron Neural Network Model for UAV Sensor Fault Detection." In 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE). IEEE, 2021. http://dx.doi.org/10.1109/iciscae52414.2021.9590669.
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Fei Yang, Wing W. Y. Ng, Eric C. C. Tsang, Xiao-Qin Zeng, and Daniel S. Yeung. "Localized generalization error model for Multilayer Perceptron Neural Networks." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620512.
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Orhan, Umut, Mahmut Hekim, and Mahmut Ozer. "Discretization approach to EEG signal classification using Multilayer Perceptron Neural Network model." In 2010 15th National Biomedical Engineering Meeting (BIYOMUT 2010). IEEE, 2010. http://dx.doi.org/10.1109/biyomut.2010.5479842.
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Yiming Tang, Jianzhong Zhao, and Wen Wu. "Analysis of quadruple-ridged square waveguide by multilayer perceptron neural network model." In 2006 Asia-Pacific Microwave Conference. IEEE, 2006. http://dx.doi.org/10.1109/apmc.2006.4429782.
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Rodriguez, Miguel Angel, John Felipe Sotomonte, Jenny Cifuentes, and Maximiliano Bueno-Lopez. "Classification of Power Quality Disturbances using Hilbert Huang Transform and a Multilayer Perceptron Neural Network Model." In 2019 International Conference on Smart Energy Systems and Technologies (SEST). IEEE, 2019. http://dx.doi.org/10.1109/sest.2019.8849114.
Full textReports on the topic "Multilayer Perceptron neural network model"
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Ramakrishnan, Aravind, Fangyu Liu, Angeli Jayme, and Imad Al-Qadi. Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Model. Illinois Center for Transportation, 2024. https://doi.org/10.36501/0197-9191/24-030.
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For robust pavement design, accurate damage computation is essential, especially for loading scenarios such as truck platoons. Studies have developed a framework to compute pavement distresses as function of lateral position, spacing, and market-penetration level of truck platoons. The established framework uses a robust 3D pavement model, along with the AASHTOWare Mechanistic–Empirical Pavement Design Guidelines (MEPDG) transfer functions to compute pavement distresses. However, transfer functions include high variability and lack physical significance. Therefore, as an improvement to effectively predict permanent deformation, this study utilized a conventional Burger’s model, incorporating a nonlinear power-law dashpot, in lieu of a transfer function. Key components, including stress increments and the Jacobian, were derived for implementation in ABAQUS as a user subroutine. Model parameters were determined through asphalt concrete (AC) flow number and dynamic modulus tests. Using a nonlinear power-law dashpot, the model accurately characterized rutting under varying conditions. The Burger’s model was both verified and validated to check the accuracy of implementation and representative of the actual behavior, respectively. Initially developed in 1D domain, the validated Burger’s model was integrated into the robust 3D finite element (FE) pavement model to predict permanent deformation. A new load-pass approach (LPA) enabled reduction in computational domain and cost, along with implementing transient loads more efficiently. The combined integration of the LPA and the Burger’s model into the pavement model effectively captured the rutting progression per loading cycle. Moreover, a graph neural network (GNN) was established to extend the prediction power of the framework, while strategically limiting the FE numerical matrix. The FE model data was transformed into a graph structure, converting FE model components into corresponding graph nodes and edges. The GNN-based pavement simulator (GPS) was developed to model 3D pavement responses, integrating three key components: encoder, processor, and decoder. The GPS model employed two-layer multilayer perceptrons (MLP) for the encoder and decoder, while utilizing graph network (GN) technology for the processor. Validation occurred through two case studies—OneStep and Rollout—with results compared against FE model data as ground truth. Results demonstrated that the GPS model provides an accurate and computationally efficient alternative to traditional 3D pavement FE simulations.
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Rivera-Casillas, Peter, and Ian Dettwiller. Neural Ordinary Differential Equations for rotorcraft aerodynamics. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48420.
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High-fidelity computational simulations of aerodynamics and structural dynamics on rotorcraft are essential for helicopter design, testing, and evaluation. These simulations usually entail a high computational cost even with modern high-performance computing resources. Reduced order models can significantly reduce the computational cost of simulating rotor revolutions. However, reduced order models are less accurate than traditional numerical modeling approaches, making them unsuitable for research and design purposes. This study explores the use of a new modified Neural Ordinary Differential Equation (NODE) approach as a machine learning alternative to reduced order models in rotorcraft applications—specifically to predict the pitching moment on a rotor blade section from an initial condition, mach number, chord velocity and normal velocity. The results indicate that NODEs cannot outperform traditional reduced order models, but in some cases they can outperform simple multilayer perceptron networks. Additionally, the mathematical structure provided by NODEs seems to favor time-dependent predictions. We demonstrate how this mathematical structure can be easily modified to tackle more complex problems. The work presented in this report is intended to establish an initial evaluation of the usability of the modified NODE approach for time-dependent modeling of complex dynamics over seen and unseen domains.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.
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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p<0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p<0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).