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Journal articles on the topic 'Neural networks (Computer science) Statistical mechanics'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

Bahri, Yasaman, Jonathan Kadmon, Jeffrey Pennington, Sam S. Schoenholz, Jascha Sohl-Dickstein, and Surya Ganguli. "Statistical Mechanics of Deep Learning." Annual Review of Condensed Matter Physics 11, no. 1 (March 10, 2020): 501–28. http://dx.doi.org/10.1146/annurev-conmatphys-031119-050745.

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The recent striking success of deep neural networks in machine learning raises profound questions about the theoretical principles underlying their success. For example, what can such deep networks compute? How can we train them? How does information propagate through them? Why can they generalize? And how can we teach them to imagine? We review recent work in which methods of physical analysis rooted in statistical mechanics have begun to provide conceptual insights into these questions. These insights yield connections between deep learning and diverse physical and mathematical topics, including random landscapes, spin glasses, jamming, dynamical phase transitions, chaos, Riemannian geometry, random matrix theory, free probability, and nonequilibrium statistical mechanics. Indeed, the fields of statistical mechanics and machine learning have long enjoyed a rich history of strongly coupled interactions, and recent advances at the intersection of statistical mechanics and deep learning suggest these interactions will only deepen going forward.

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Zhang, Lifu, and Tarek S. Abdelrahman. "Pipelined Training with Stale Weights in Deep Convolutional Neural Networks." Applied Computational Intelligence and Soft Computing 2021 (September 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/3839543.

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The growth in size and complexity of convolutional neural networks (CNNs) is forcing the partitioning of a network across multiple accelerators during training and pipelining of backpropagation computations over these accelerators. Pipelining results in the use of stale weights. Existing approaches to pipelined training avoid or limit the use of stale weights with techniques that either underutilize accelerators or increase training memory footprint. This paper contributes a pipelined backpropagation scheme that uses stale weights to maximize accelerator utilization and keep memory overhead modest. It explores the impact of stale weights on the statistical efficiency and performance using 4 CNNs (LeNet-5, AlexNet, VGG, and ResNet) and shows that when pipelining is introduced in early layers, training with stale weights converges and results in models with comparable inference accuracies to those resulting from nonpipelined training (a drop in accuracy of 0.4%, 4%, 0.83%, and 1.45% for the 4 networks, respectively). However, when pipelining is deeper in the network, inference accuracies drop significantly (up to 12% for VGG and 8.5% for ResNet-20). The paper also contributes a hybrid training scheme that combines pipelined with nonpipelined training to address this drop. The potential for performance improvement of the proposed scheme is demonstrated with a proof-of-concept pipelined backpropagation implementation in PyTorch on 2 GPUs using ResNet-56/110/224/362, achieving speedups of up to 1.8X over a 1-GPU baseline.

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Fu, Jinlong, Shaoqing Cui, Song Cen, and Chenfeng Li. "Statistical characterization and reconstruction of heterogeneous microstructures using deep neural network." Computer Methods in Applied Mechanics and Engineering 373 (January 2021): 113516. http://dx.doi.org/10.1016/j.cma.2020.113516.

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Machrowska, Anna, Jakub Szabelski, Robert Karpiński, Przemysław Krakowski, Józef Jonak, and Kamil Jonak. "Use of Deep Learning Networks and Statistical Modeling to Predict Changes in Mechanical Parameters of Contaminated Bone Cements." Materials 13, no. 23 (November 28, 2020): 5419. http://dx.doi.org/10.3390/ma13235419.

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The purpose of the study was to test the usefulness of deep learning artificial neural networks and statistical modeling in predicting the strength of bone cements with defects. The defects are related to the introduction of admixtures, such as blood or saline, as contaminants into the cement at the preparation stage. Due to the wide range of applications of deep learning, among others in speech recognition, bioinformation processing, and medication design, the extent was checked to which it is possible to obtain information related to the prediction of the compressive strength of bone cements. Development and improvement of deep learning network (DLN) algorithms and statistical modeling in the analysis of changes in the mechanical parameters of the tested materials will enable determining an acceptable margin of error during surgery or cement preparation in relation to the expected strength of the material used to fill bone cavities. The use of the abovementioned computer methods may, therefore, play a significant role in the initial qualitative assessment of the effects of procedures and, thus, mitigation of errors resulting in failure to maintain the required mechanical parameters and patient dissatisfaction.

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Takahashi, Shuntaro, and Kumiko Tanaka-Ishii. "Evaluating Computational Language Models with Scaling Properties of Natural Language." Computational Linguistics 45, no. 3 (September 2019): 481–513. http://dx.doi.org/10.1162/coli_a_00355.

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In this article, we evaluate computational models of natural language with respect to the universal statistical behaviors of natural language. Statistical mechanical analyses have revealed that natural language text is characterized by scaling properties, which quantify the global structure in the vocabulary population and the long memory of a text. We study whether five scaling properties (given by Zipf’s law, Heaps’ law, Ebeling’s method, Taylor’s law, and long-range correlation analysis) can serve for evaluation of computational models. Specifically, we test n-gram language models, a probabilistic context-free grammar, language models based on Simon/Pitman-Yor processes, neural language models, and generative adversarial networks for text generation. Our analysis reveals that language models based on recurrent neural networks with a gating mechanism (i.e., long short-term memory; a gated recurrent unit; and quasi-recurrent neural networks) are the only computational models that can reproduce the long memory behavior of natural language. Furthermore, through comparison with recently proposed model-based evaluation methods, we find that the exponent of Taylor’s law is a good indicator of model quality.

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Krč, Rostislav, Jan Podroužek, Martina Kratochvílová, Ivan Vukušič, and Otto Plášek. "Neural Network-Based Train Identification in Railway Switches and Crossings Using Accelerometer Data." Journal of Advanced Transportation 2020 (November 24, 2020): 1–10. http://dx.doi.org/10.1155/2020/8841810.

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This paper aims to analyse possibilities of train type identification in railway switches and crossings (S&C) based on accelerometer data by using contemporary machine learning methods such as neural networks. That is a unique approach since trains have been only identified in a straight track. Accelerometer sensors placed around the S&C structure were the source of input data for subsequent models. Data from four S&C at different locations were considered and various neural network architectures evaluated. The research indicated the feasibility to identify trains in S&C using neural networks from accelerometer data. Models trained at one location are generally transferable to another location despite differences in geometrical parameters, substructure, and direction of passing trains. Other challenges include small dataset and speed variation of the trains that must be considered for accurate identification. Results are obtained using statistical bootstrapping and are presented in a form of confusion matrices.

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Kim, D. H., D. J. Kim, and B. M. Kim. "The Application of Neural Networks and Statistical Methods to Process Design in Metal Forming Processes." International Journal of Advanced Manufacturing Technology 15, no. 12 (December 6, 1999): 886–94. http://dx.doi.org/10.1007/s001700050146.

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Pled, Florent, Christophe Desceliers, and Tianyu Zhang. "A robust solution of a statistical inverse problem in multiscale computational mechanics using an artificial neural network." Computer Methods in Applied Mechanics and Engineering 373 (January 2021): 113540. http://dx.doi.org/10.1016/j.cma.2020.113540.

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Gao, Zhenyi, Bin Zhou, Chunge Ju, Qi Wei, Xinxi Zhang, and Rong Zhang. "Online Nonlinear Error Compensation Circuit Based on Neural Networks." Machines 9, no. 8 (July 31, 2021): 151. http://dx.doi.org/10.3390/machines9080151.

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Nonlinear errors of sensor output signals are common in the field of inertial measurement and can be compensated with statistical models or machine learning models. Machine learning solutions with large computational complexity are generally offline or implemented on additional hardware platforms, which are difficult to meet the high integration requirements of microelectromechanical system inertial sensors. This paper explored the feasibility of an online compensation scheme based on neural networks. In the designed solution, a simplified small-scale network is used for modeling, and the peak-to-peak value and standard deviation of the error after compensation are reduced to 17.00% and 16.95%, respectively. Additionally, a compensation circuit is designed based on the simplified modeling scheme. The results show that the circuit compensation effect is consistent with the results of the algorithm experiment. Under SMIC 180 nm complementary metal-oxide semiconductor (CMOS) technology, the circuit has a maximum operating frequency of 96 MHz and an area of 0.19 mm2. When the sampling signal frequency is 800 kHz, the power consumption is only 1.12 mW. This circuit can be used as a component of the measurement and control system on chip (SoC), which meets real-time application scenarios with low power consumption requirements.

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Quiza, Ramón, Luis Figueira, and J. Paulo Davim. "Comparing statistical models and artificial neural networks on predicting the tool wear in hard machining D2 AISI steel." International Journal of Advanced Manufacturing Technology 37, no. 7-8 (March 28, 2007): 641–48. http://dx.doi.org/10.1007/s00170-007-0999-7.

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Kalinin, Maxim, Vasiliy Krundyshev, and Peter Zegzhda. "Cybersecurity Risk Assessment in Smart City Infrastructures." Machines 9, no. 4 (April 4, 2021): 78. http://dx.doi.org/10.3390/machines9040078.

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The article is devoted to cybersecurity risk assessment of the dynamic device-to-device networks of a smart city. Analysis of the modern security threats at the IoT/IIoT, VANET, and WSN inter-device infrastructures demonstrates that the main concern is a set of network security threats targeted at the functional sustainability of smart urban infrastructure, the most common use case of smart networks. As a result of our study, systematization of the existing cybersecurity risk assessment methods has been provided. Expert-based risk assessment and active human participation cannot be provided for the huge, complex, and permanently changing digital environment of the smart city. The methods of scenario analysis and functional analysis are specific to industrial risk management and are hardly adaptable to solving cybersecurity tasks. The statistical risk evaluation methods force us to collect statistical data for the calculation of the security indicators for the self-organizing networks, and the accuracy of this method depends on the number of calculating iterations. In our work, we have proposed a new approach for cybersecurity risk management based on object typing, data mining, and quantitative risk assessment for the smart city infrastructure. The experimental study has shown us that the artificial neural network allows us to automatically, unambiguously, and reasonably assess the cyber risk for various object types in the dynamic digital infrastructures of the smart city.

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Chen, Ting, Liqing Li, and Xiubao Huang. "Predicting the fibre diameter of melt blown nonwovens: comparison of physical, statistical and artificial neural network models." Modelling and Simulation in Materials Science and Engineering 13, no. 4 (May 3, 2005): 575–84. http://dx.doi.org/10.1088/0965-0393/13/4/008.

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Sivasankaran, S., R. Narayanasamy, T. Ramesh, and M. Prabhakar. "Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique." Computational Materials Science 47, no. 1 (November 2009): 46–59. http://dx.doi.org/10.1016/j.commatsci.2009.06.013.

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14

Li, Y., R. L. Mahajan, and N. Nikmanesh. "Fine Pitch Stencil Printing Process Modeling and Optimization." Journal of Electronic Packaging 118, no. 1 (March 1, 1996): 1–6. http://dx.doi.org/10.1115/1.2792121.

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In this paper, we present a statistical-neural network modeling approach to process optimization of fine pitch stencil printing for solder paste deposition on pads of printed circuit boards (PCB). The overall objective was to determine the optimum settings of the design parameters that would result in minimum solder paste height variation for the new board designs with 20-mil, 25-mil, and 50-mil pitch pad patterns. As a first step, a Taguchi orthogonal array, L27, was designed to capture the main effects of the six important printing machinery parameters and the PCBs pad conditions. Some of their interactions were also included. Fifty-four experimental runs (two per setting) were conducted. These data were then used to construct neural network models relating the desired quality characteristics to the input design parameters. Our modular approach was used to select the appropriate architecture for these models. These models in conjunction with the gradient descent algorithm enabled us to determine the optimum settings for minimum solder paste height variation. Confirming experiments on the production line validated the optimum settings predicted by the model. In addition to the combination of all the three pad patterns, i.e., 20, 25, and 50 mil pitch pads, we also built neural network models for individual and dual combinations of the three pad patterns. The simulations indicate different optimum settings for different pad pattern combinations.

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MJOLSNESS, ERIC. "ON COOPERATIVE QUASI-EQUILIBRIUM MODELS OF TRANSCRIPTIONAL REGULATION." Journal of Bioinformatics and Computational Biology 05, no. 02b (April 2007): 467–90. http://dx.doi.org/10.1142/s0219720007002874.

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Mechanistic models for transcriptional regulation are derived using the methods of equilibrium statistical mechanics, to model equilibrating processes that occur at a fast time scale. These processes regulate slower changes in the synthesis and expression of transcription factors that feed back and cooperatively regulate transcription, forming a gene regulation network (GRN). We rederive and extend two previous quasi-equilibrium models of transcriptional regulation, and demonstrate circumstances under which they can be approximated at each transcription complex by feed-forward artificial neural network (ANN) models. A single-level mechanistic model can be approximated by a successfully applied phenomenological model of GRNs which is based on single-layer analog-valued ANNs. A two-level hierarchical mechanistic model, with separate activation states for modules and for the whole transcription complex, can be approximated by a two-layer feed-forward ANN in several related ways. The sufficient conditions demonstrated for the ANN approximations correspond biologically to large numbers of binding sites each of which have a small effect. A further extension to the single-level and two-level models allows one-dimensional chains of overlapping and/or energetically interacting binding sites within a module. Partition functions for these models can be constructed from stylized diagrams that indicate energetic and logical interactions between binary-valued state variables. All parameters in the mechanistic models, including the two approximations, can in principle be related to experimentally measurable free energy differences, among other observables.

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Grenson, Pierre, and Eric Garnier. "Distortion reconstruction in S-ducts from wall static pressure measurements." International Journal of Numerical Methods for Heat & Fluid Flow 28, no. 5 (May 8, 2018): 1134–55. http://dx.doi.org/10.1108/hff-06-2017-0232.

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Purpose This paper aims to report the attempts for predicting “on-the-fly” flow distortion in the engine entrance plane of a highly curved S-duct from wall static pressure measurements. Such a technology would be indispensable to trigger active flow control devices to mitigate the intense flow separations which occur in specific flight conditions. Design/methodology/approach Evaluation of different reconstruction algorithms is performed on the basis of data extracted from a Zonal Detached Eddy Simulation (ZDES) of a well-documented S-Duct (Garnier et al., AIAA J., 2015). Contrary to RANS methods, such a hybrid approach makes unsteady distortions available, which are necessary information for reconstruction algorithm assessment. Findings The best reconstruction accuracy is obtained with the artificial neural network (ANN) but the improvement compared to the classical linear stochastic estimation (LSE) is minor. The different inlet distortion coefficients are not reconstructed with the same accuracy. KA2 coefficient is finally identified as the more suited for activation of the control device. Originality/value LSE and its second-order variant (quadratic stochastic estimation [QSE]) are applied for reconstructing instantaneous stagnation pressure in the flow field. The potential improvement of an algorithm based on an ANN is also evaluated. The statistical link between the wall sensors and 40-Kulite rake sensors are carefully discussed and the accuracy of the reconstruction of the most used distortion coefficients (DC60, RDI, CDI and KA2) is quantified for each estimation technique.

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Kozlenko, Mykola, Olena Zamikhovska, and Leonid Zamikhovskyi. "Software implemented fault diagnosis of natural gas pumping unit based on feedforward neural network." Eastern-European Journal of Enterprise Technologies 2, no. 2 (110) (April 30, 2021): 99–109. http://dx.doi.org/10.15587/1729-4061.2021.229859.

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In recent years, more and more attention has been paid to the use of artificial neural networks (ANN) for the diagnostics of gas pumping units (GPU). Usually, ANN training is carried out on GPU workflow models, and generated sets of diagnostic data are used to simulate defect conditions. At the same time, the results obtained do not allow assessing the real state of the GPU. It is proposed to use the characteristics of the acoustic and vibration processes of the GPU as the input data of the ANN. A descriptive statistical analysis of real vibration and acoustic processes generated by the operation of the GPU type GTK-25-i (Nuovo Pignone, Italy) was carried out. The formation of batches of diagnostic features arriving at the input of the ANN was carried out. Diagnostic features are the five maximum amplitude components of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic features are calculated directly in the ANN input data pipeline in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedforward ANN, trained on the backpropagation algorithm. The results of training and testing the developed ANN are presented. During testing, it was found that the signal classification precision for the “nominal” state of all 1,475 signal samples is 1.0000, for the “current” state, precision equals 0.9853, and for the “defective” state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power

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Yagawa, G., and H. Okuda. "Neural networks in computational mechanics." Archives of Computational Methods in Engineering 3, no. 4 (December 1996): 435–512. http://dx.doi.org/10.1007/bf02818935.

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Hurtado, Jorge E. "Neural networks in stochastic mechanics." Archives of Computational Methods in Engineering 8, no. 3 (September 2001): 303–42. http://dx.doi.org/10.1007/bf02736646.

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Kumar, Yogesh, Shashi Kant Verma, and Sandeep Sharma. "Quantum-inspired binary gravitational search algorithm to recognize the facial expressions." International Journal of Modern Physics C 31, no. 10 (August 14, 2020): 2050138. http://dx.doi.org/10.1142/s0129183120501387.

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This paper addresses an autonomous facial expression recognition system using the feature selection approach of the Quantum-Inspired Binary Gravitational Search Algorithm (QIBGSA). The detection of facial features completely depends upon the selection of precise features. The concept of QIBGSA is a modified binary version of the gravitational search algorithm by mimicking the properties of quantum mechanics. The QIBGSA approach reduces the computation cost for the initial extracted feature set using the hybrid approach of Local binary patterns with Gabor filter method. The proposed automated system is a sequential system with experimentation on the image-based dataset of Karolinska Directed Emotional Faces (KDEF) containing human faces with seven different emotions and different yaw angles. The experiments are performed to find out the optimal emotions using the feature selection approach of QIBGSA and classification using a deep convolutional neural network for robust and efficient facial expression recognition. Also, the effect of variations in the yaw angle (front to half side view) on facial expression recognition is studied. The results of the proposed system for the KDEF dataset are determined in three different cases of frontal view, half side view, and combined frontal and half side view images. The system efficacy is analyzed in terms of recognition rate.

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Murtagh, F. M. "Neural networks for statistical and economic data." Neurocomputing 3, no. 1 (August 1991): 51–52. http://dx.doi.org/10.1016/0925-2312(91)90019-8.

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Gielen, C. "Artificial neural networks and statistical pattern recognition." Neurocomputing 4, no. 6 (December 1992): 325–28. http://dx.doi.org/10.1016/0925-2312(92)90017-j.

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Adelsberger-Mangan, Dawn M., and William B. Levy. "Information maintenance and statistical dependence reduction in simple neural networks." Biological Cybernetics 67, no. 5 (September 1992): 469–77. http://dx.doi.org/10.1007/bf00200991.

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Wu, Mengmeng, and Jianfeng Wang. "Estimating Contact Force Chains Using Artificial Neural Network." Applied Sciences 11, no. 14 (July 7, 2021): 6278. http://dx.doi.org/10.3390/app11146278.

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The inhomogeneous distribution of contact force chains (CFC) in quasi-statically sheared granular materials dominates their bulk mechanical properties. Although previous micromechanical investigations have gained significant insights into the statistical and spatial distribution of CFC, they still lack the capacity to quantitatively estimate CFC evolution in a sheared granular system. In this paper, an artificial neural network (ANN) based on discrete element method (DEM) simulation data is developed and applied to predict the anisotropy of CFC in an assembly of spherical grains undergoing a biaxial test. Five particle-scale features including particle size, coordination number, x- and y-velocity (i.e., x and y-components of the particle velocity), and spin, which all contain predictive information about the CFC, are used to establish the ANN. The results of the model prediction show that the combined features of particle size and coordination number have a dominating influence on the CFC’s estimation. An excellent model performance manifested in a close match between the rose diagrams of the CFC from the ANN predictions and DEM simulations is obtained with a mean accuracy of about 0.85. This study has shown that machine learning is a promising tool for studying the complex mechanical behaviors of granular materials.

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Addeh, Jalil, Ata Ebrahimzadeh, Milad Azarbad, and Vahid Ranaee. "Statistical process control using optimized neural networks: A case study." ISA Transactions 53, no. 5 (September 2014): 1489–99. http://dx.doi.org/10.1016/j.isatra.2013.07.018.

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Leger, R. P., Wm J. Garland, and W. F. S. Poehlman. "Fault detection and diagnosis using statistical control charts and artificial neural networks." Artificial Intelligence in Engineering 12, no. 1-2 (January 1998): 35–47. http://dx.doi.org/10.1016/s0954-1810(96)00039-8.

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Shen, Lin, and Weitao Yang. "Molecular Dynamics Simulations with Quantum Mechanics/Molecular Mechanics and Adaptive Neural Networks." Journal of Chemical Theory and Computation 14, no. 3 (February 13, 2018): 1442–55. http://dx.doi.org/10.1021/acs.jctc.7b01195.

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Jain, A. K., and J. Mao. "Guest Editorial Special Issue on Artificial Neural Networks and Statistical Pattern Recognition." IEEE Transactions on Neural Networks 8, no. 1 (January 1997): 1–4. http://dx.doi.org/10.1109/tnn.1997.554186.

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Malmgren-Hansen, David, Valero Laparra, Allan Aasbjerg Nielsen, and Gustau Camps-Valls. "Statistical retrieval of atmospheric profiles with deep convolutional neural networks." ISPRS Journal of Photogrammetry and Remote Sensing 158 (December 2019): 231–40. http://dx.doi.org/10.1016/j.isprsjprs.2019.10.002.

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Bassi, Danilo, and Oscar Olivares. "Medium Term Electric Load Forecasting Using TLFN Neural Networks." International Journal of Computers Communications & Control 1, no. 2 (April 1, 2006): 23. http://dx.doi.org/10.15837/ijccc.2006.2.2282.

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This paper develops medium term electric load forecasting using neural networks, based on historical series of electric load, economic and demographic variables. The neural network chosen for this work is the Time Lagged Feedforward Network (TLFN), which combines conventional network topology (multilayer perceptron) with good handling of time dependencies by means of gamma memory. This is a versatile mechanism that generalizes the short term structures of memory, based on delays and recurrences. This scheme allows smaller adjustments without requiring changes in the general network structure. The neural model gave satisfactory results exceeding those obtained by classical statistical models like multiple linear regression.

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Aldakheel, Fadi, Ramish Satari, and Peter Wriggers. "Feed-Forward Neural Networks for Failure Mechanics Problems." Applied Sciences 11, no. 14 (July 14, 2021): 6483. http://dx.doi.org/10.3390/app11146483.

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This work addresses an efficient neural network (NN) representation for the phase-field modeling of isotropic brittle fracture. In recent years, data-driven approaches, such as neural networks, have become an active research field in mechanics. In this contribution, deep neural networks—in particular, the feed-forward neural network (FFNN)—are utilized directly for the development of the failure model. The verification and generalization of the trained models for elasticity as well as fracture behavior are investigated by several representative numerical examples under different loading conditions. As an outcome, promising results close to the exact solutions are produced.

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Levy, William B., Hakan Deliç, and Dawn M. Adelsberger-Mangan. "The statistical relationship between connectivity and neural activity in fractionally connected feed-forward networks." Biological Cybernetics 80, no. 2 (February 10, 1999): 131–39. http://dx.doi.org/10.1007/s004220050511.

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Rigatos, G., N. Zervos, M. Abbaszadeh, P. Siano, D. Serpanos, and V. Siadimas. "Neural networks and statistical decision making for fault diagnosis of PM linear synchronous machines." International Journal of Systems Science 51, no. 12 (August 4, 2020): 2150–66. http://dx.doi.org/10.1080/00207721.2020.1792579.

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Del Prete, V., and A. C. C. Coolen. "Non-equilibrium statistical mechanics of recurrent networks with realistic neurons." Neurocomputing 58-60 (June 2004): 239–44. http://dx.doi.org/10.1016/j.neucom.2004.01.050.

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Awad, Mohammed, and Mohammed Zaid-Alkelani. "Prediction of Water Demand Using Artificial Neural Networks Models and Statistical Model." International Journal of Intelligent Systems and Applications 11, no. 9 (September 8, 2019): 40–55. http://dx.doi.org/10.5815/ijisa.2019.09.05.

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Li, J., and K. C. Gupta. "Mechanism Design with MP-Neural Networks." Journal of Mechanical Design 120, no. 4 (December 1, 1998): 527–32. http://dx.doi.org/10.1115/1.2829310.

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The prevalent Mathematical Programming Neural Network (MPNN) models are surveyed, and MPNN models have been developed and applied to the unconstrained optimization of mechanisms. Algorithms which require Hessian inversion and those which build up a variable approach matrix, are investigated. Based upon a comprehensive investigation of the Augmented Lagrange Multiplier (ALM) method, new algorithms have been developed from the combination of ideas from MPNN and ALM methods and applied to the constrained optimization of mechanisms. A relationship between the weighted least square minimization of design equation error residuals and the mini-max norm of the structure error for function generating mechanisms is developed and employed in the optimization process; as a result, the computational difficulties arising from the direct usage of the complex structural error function have been avoided. The paper presents relevant theory as well as some numerical experience for four MPNN algorithms.

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Specht, Luciano Pivoto, Oleg Khatchatourian, Lélio Antônio Teixeira Brito, and Jorge Augusto Pereira Ceratti. "Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks." Materials Research 10, no. 1 (March 2007): 69–74. http://dx.doi.org/10.1590/s1516-14392007000100015.

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Bozic, Jovana, and Djordje Babic. "EUR/RSD exchange rate forecasting using hybrid wavelet-neural model: A case study." Computer Science and Information Systems 12, no. 2 (2015): 487–508. http://dx.doi.org/10.2298/csis140728005b.

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In this paper, we examine and discuss modeling and prediction results of several exchange rates, with main focus on EUR/RSD, using a combination of wavelet transforms, neural networks and statistical time series analytical techniques. We have also designed a user friendly software prediction tool in MATLwhich implements the proposed model. The analyzed hybrid model combines the capabilities of two different wavelet transforms and neural networks that can capture hidden but crucial structure attributes embedded in the exchange rate. The financial time series is decomposed into a wavelet representation using two different resolution levels. For each of the new time series, a neural network is created, trained and used for prediction. In order to create an aggregate forecast, the individual predictions are combined with statistical features extracted from the original input. Additional to the conclusion that the increase in resolution level does not improve the prediction accuracy, the analysis of obtained results indicates that the suggested model sufficiently satisfies characteristics of a financial predictor.

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Chetouani, Yahya. "Using neural networks and statistical tests for detecting changes in the process dynamics." International Journal of Modelling, Identification and Control 3, no. 2 (2008): 113. http://dx.doi.org/10.1504/ijmic.2008.019349.

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Kitahara, Masashi, Taichi Hayasaka, Naohiro Toda, and Shiro Usui. "On the statistical properties of least-square estimators of layered neural networks." Systems and Computers in Japan 35, no. 12 (2004): 1–9. http://dx.doi.org/10.1002/scj.10580.

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Kitahara, Masashi, Taichi Hayasaka, and Shiro Usui. "On the statistical behavior of the learning error of layered neural networks." Systems and Computers in Japan 36, no. 8 (2005): 49–58. http://dx.doi.org/10.1002/scj.20301.

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Gorunescu, Florin, Marina Gorunescu, Elia El-Darzi, and Smaranda Gorunescu. "A statistical framework for evaluating neural networks to predict recurrent events in breast cancer." International Journal of General Systems 39, no. 5 (July 2010): 471–88. http://dx.doi.org/10.1080/03081079.2010.484282.

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Manuel, G., and J. H. C. Pretorius. "The significance of relevance trees in the identification of artificial neural networks input vectors." Journal of Energy in Southern Africa 24, no. 1 (February 1, 2013): 27–34. http://dx.doi.org/10.17159/2413-3051/2013/v24i1a3004.

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In the 1980s a renewed interest in artificial neural networks (ANN) has led to a wide range of applications which included demand forecasting. ANN demand forecasting algorithms were found to be preferable over parametric or also referred to as statistical based techniques. For an ANN demand forecasting algorithm, the demand may be stochastic or deterministic, linear or nonlinear. Comparative studies conducted on the two broad streams of demand forecasting methodologies, namely artificial intelligence methods and statistical methods has revealed that AI methods tend to hide the complexities of correlation analysis. In parametric methods, correlation is found by means of sometimes difficult and rigorous mathematics. Most statistical methods extract and correlate various demand elements which are usually broadly classed into weather and non-weather variables. Several models account for noise and random factors and suggest optimization techniques specific to certain model parameters. However, for an ANN algorithm, the identification of input and output vectors is critical. Predicting the future demand is conducted by observing previous demand values and how underlying factors influence the overall demand. Trend analyses are conducted on these influential variables and a medium and long term forecast model is derived. In order to perform an accurate forecast, the changes in the demand have to be defined in terms of how these input vectors correlate to the final demand. The elements of the input vectors have to be identifiable and quantifiable. This paper proposes a method known as relevance trees to identify critical elements of the input vector. The case study is of a rapid railway operator, namely the Gautrain.

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NACHEV, ANATOLI, SEAMUS HILL, CHRIS BARRY, and BORISLAV STOYANOV. "FUZZY, DISTRIBUTED, INSTANCE COUNTING, AND DEFAULT ARTMAP NEURAL NETWORKS FOR FINANCIAL DIAGNOSIS." International Journal of Information Technology & Decision Making 09, no. 06 (November 2010): 959–78. http://dx.doi.org/10.1142/s0219622010004111.

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This paper shows the potential of neural networks based on the Adaptive Resonance Theory as tools that generate warning signals when bankruptcy of a company is expected (bankruptcy prediction problem). Using that class of neural networks is still unexplored to date. We examined four of the most popular networks of the class — fuzzy, distributed, instance counting, and default ARTMAP. In order to illustrate their performance and to compare with other techniques, we used data, financial ratios, and experimental conditions identical to those published in previous studies. Our experiments show that two financial ratios provide highest discriminatory power of the model and ensure best prediction accuracy. We examined performance and validated results by exhaustive search of input variables, cross-validation, receiver operating characteristic analysis, and area under curve metric. We also did application-specific cost analysis. Our results show that distributed ARTMAP outperforms the other three models in general, but the fuzzy model is best performer for certain vigilance values and in the application-specific context. We also found that ARTMAP outperforms the most popular neural networks — multi-layer perceptrons and other statistical techniques applied to the same data.

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Li, Lei, Min Feng, Lianwen Jin, Shenjin Chen, Lihong Ma, and Jiakai Gao. "Domain Knowledge Embedding Regularization Neural Networks for Workload Prediction and Analysis in Cloud Computing." Journal of Information Technology Research 11, no. 4 (October 2018): 137–54. http://dx.doi.org/10.4018/jitr.2018100109.

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Online services are now commonly deployed via cloud computing based on Infrastructure as a Service (IaaS) to Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). However, workload is not constant over time, so guaranteeing the quality of service (QoS) and resource cost-effectiveness, which is determined by on-demand workload resource requirements, is a challenging issue. In this article, the authors propose a neural network-based-method termed domain knowledge embedding regularization neural networks (DKRNN) for large-scale workload prediction. Based on analyzing the statistical properties of a real large-scale workload, domain knowledge, which provides extended information about workload changes, is embedded into artificial neural networks (ANN) for linear regression to improve prediction accuracy. Furthermore, the regularization with noisy is combined to improve the generalization ability of artificial neural networks. The experiments demonstrate that the model can achieve more accuracy of workload prediction, provide more adaptive resource for higher resource cost effectiveness and have less impact on the QoS.

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Duggal, Ashmeet Kaur, and Meenu Dave Dr. "INTELLIGENT IDENTITY AND ACCESS MANAGEMENT USING NEURAL NETWORKS." Indian Journal of Computer Science and Engineering 12, no. 1 (February 20, 2021): 47–56. http://dx.doi.org/10.21817/indjcse/2021/v12i1/211201154.

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Fang, Yin-Ying, Chi-Fang Chen, and Sheng-Ju Wu. "Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan." ANZIAM Journal 59 (July 25, 2019): C318—C357. http://dx.doi.org/10.21914/anziamj.v59i0.12655.

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Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets. References Nathan D Merchant, Kurt M Fristrup, Mark P Johnson, Peter L Tyack, Matthew J Witt, Philippe Blondel, and Susan E Parks. Measuring acoustic habitats. Methods in Ecology and Evolution, 6(3):257265, 2015. doi:10.1111/2041-210X.12330. Nathan D Merchant, Philippe Blondel, D Tom Dakin, and John Dorocicz. Averaging underwater noise levels for environmental assessment of shipping. The Journal of the Acoustical Society of America, 132(4):EL343EL349, 2012. doi:10.1121/1.4754429. Chi-Fang Chen, Hsiang-Chih Chan, Ray-I Chang, Tswen-Yung Tang, Sen Jan, Chau-Chang Wang, Ruey-Chang Wei, Yiing-Jang Yang, Lien-Siang Chou, Tzay-Chyn Shin, et al. Data demonstrations on physical oceanography and underwater acoustics from the marine cable hosted observatory (macho). In OCEANS, 2012-Yeosu, pages 16. IEEE, 2012. doi:10.1109/OCEANS-Yeosu.2012.6263639. Sauda Sadaf P Yashaswini, Soumya Halagur, Fazil Khan, and Shanta Rangaswamy. A literature survey on ambient noise analysis for underwater acoustic signals. International Journal of Computer Engineering and Sciences, 1(7):19, 2015. doi:10.26472/ijces.v1i7.37. Shuguang Wang and Xiangyang Zeng. Robust underwater noise targets classification using auditory inspired time-frequency analysis. Applied Acoustics, 78:6876, 2014. doi:10.1016/j.apacoust.2013.11.003. LG Weiss and TL Dixon. Wavelet-based denoising of underwater acoustic signals. The Journal of the Acoustical Society of America, 101(1):377383, 1997. doi:10.1121/1.417983. Timothy Alexis Bodisco, Jason D'Netto, Neil Kelson, Jasmine Banks, Ross Hayward, and Tony Parker. Characterising an ecg signal using statistical modelling: a feasibility study. ANZIAM Journal, 55:3246, 2014. doi:10.21914/anziamj.v55i0.7818. José Ribeiro-Fonseca and Luís Correia. Identification of underwater acoustic noise. In OCEANS'94.'Oceans Engineering for Today's Technology and Tomorrow's Preservation.'Proceedings, volume 2, pages II/597II/602 vol. 2. IEEE. Linus YS Chiu and Hwei-Ruy Chen. Estimation and reduction of effects of sea surface reflection on underwater vertical channel. In Underwater Technology Symposium (UT), 2013 IEEE International, pages 18. IEEE, 2013. doi:10.1109/UT.2013.6519874. G.M. Wenz. Acoustic ambient noise in the ocean: spectra and sources. Thesis, 1962. doi:10.1121/1.1909155. Donald Ross. Mechanics of underwater noise. Elsevier, 2013. doi:10.1121/1.398685. Chris Drummond and Robert C Holte. Exploiting the cost (in) sensitivity of decision tree splitting criteria. In ICML, volume 1, 2000. Charles Elkan. The foundations of cost-sensitive learning. In International joint conference on artificial intelligence, volume 17, pages 973978. Lawrence Erlbaum Associates Ltd, 2001. Chris Gillard, Alexei Kouzoubov, Simon Lourey, Alice von Trojan, Binh Nguyen, Shane Wood, and Jimmy Wang. Automatic classification of active sonar echoes for improved target identification. Douglas C Montgomery. Design and analysis of experiments. John wiley and sons, 2017. doi:10.1002/9781118147634. G Taguchi. Off-line and on-line quality control systems. In Proceedings of International Conference on Quality Control, 1978. Sheng-Ju Wu, Sheau-Wen Shiah, and Wei-Lung Yu. Parametric analysis of proton exchange membrane fuel cell performance by using the taguchi method and a neural network. Renewable Energy, 34(1):135144, 2009. doi:10.1016/j.renene.2008.03.006. Genichi Taguchi. Introduction to quality engineering: designing quality into products and processes. Technical report, 1986. doi:10.1002/qre.4680040216. Richard Horvath, Gyula Matyasi, and Agota Dregelyi-Kiss. Optimization of machining parameters for fine turning operations based on the response surface method. ANZIAM Journal, 55:250265, 2014. doi:10.21914/anziamj.v55i0.7865. Chuan-Tien Li, Sheng-Ju Wu, and Wei-Lung Yu. Parameter design on the multi-objectives of pem fuel cell stack using an adaptive neuro-fuzzy inference system and genetic algorithms. International Journal of Hydrogen Energy, 39(9):45024515, 2014. doi:10.1016/j.ijhydene.2014.01.034. Antoine Guisan, Thomas C Edwards Jr, and Trevor Hastie. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological modelling, 157(2-3):89100, 2002. doi:10.1016/S0304-3800(02)00204-1. Sheng Chen, Colin FN Cowan, and Peter M Grant. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on neural networks, 2(2):302309, 1991. doi:10.1109/72.80341. Howard Demuth and Mark Beale. Neural network toolbox for use with matlab-user's guide verion 4.0. 1993. Janice Gaffney, Charles Pearce, and David Green. Binary versus real coding for genetic algorithms: A false dichotomy? ANZIAM Journal, 51:347359, 2010. doi:10.21914/anziamj.v51i0.2776. Daniel May and Muttucumaru Sivakumar. Techniques for predicting total phosphorus in urban stormwater runoff at unmonitored catchments. ANZIAM Journal, 45:296309, 2004. doi:10.21914/anziamj.v45i0.889. Chang-Xue Jack Feng, Zhi-Guang Yu, and Andrew Kusiak. Selection and validation of predictive regression and neural network models based on designed experiments. IIE Transactions, 38(1):1323, 2006. doi:10.1080/07408170500346378. Yin-Ying Fang, Ping-Jung Sung, Kai-An Cheng, Meng Fan Tsai, and Chifang Chen. Underwater radiated noise measurement of ocean researcher 3. In The 29th Taiwan Society of Naval Architects and Marine Engineers Conference, 2017. Yin-Ying Fang, Chi-Fang Chen, and Sheng-Ju Wu. Analysis of vibration and underwater radiated noise of ocean researcher 3. In The 30th Taiwan Society of Naval Architects and Marine Engineers Conference, 2018. Det Norske Veritas. Rules for classification of ships new buildings special equipment and systems additional class part 6 chapter 24 silent class notation. Rules for Classification of ShipsNewbuildings, 2010. Underwater acousticsquantities and procedures for description and measurement of underwater sound from ships-part 1requirements for precision measurements in deep water used for comparison purposes. (ISO 17208-1:2012), 2012. Bureau Veritas. Underwater radiated noise, rule note nr 614 dt r00 e. Bureau Veritas, 2014. R.J. Urick. Principles of underwater sound, volume 3. McGraw-Hill New York, 1983. Lars Burgstahler and Martin Neubauer. New modifications of the exponential moving average algorithm for bandwidth estimation. In Proc. of the 15th ITC Specialist Seminar, 2002. Bishnu Prasad Lamichhane. Removing a mixture of gaussian and impulsive noise using the total variation functional and split bregman iterative method. ANZIAM Journal, 56:5267, 2015. doi:10.21914/anziamj.v56i0.9316. Chao-Ton Su. Quality engineering: off-line methods and applications. CRC press, 2016. Jiju Antony and Mike Kaye. Experimental quality: a strategic approach to achieve and improve quality. Springer Science and Business Media, 2012. Ozkan Kucuk, Tayeb Elfarah, Serkan Islak, and Cihan Ozorak. Optimization by using taguchi method of the production of magnesium-matrix carbide reinforced composites by powder metallurgy method. Metals, 7(9):352, 2017. doi:10.3390/met7090352. G Taguchi. System of experimental design, quality resources. New York, 108, 1987. Gavin C Cawley and Nicola LC Talbot. Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. Pattern Recognition, 36(11):25852592, 2003. doi:10.1016/S0031-3203(03)00136-5.

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Holmstrom, L., P. Koistinen, J. Laaksonen, and E. Oja. "Neural and statistical classifiers-taxonomy and two case studies." IEEE Transactions on Neural Networks 8, no. 1 (January 1997): 5–17. http://dx.doi.org/10.1109/72.554187.

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CARVALHAES, C. G., A. T. COSTA, and T. J. P. PENNA. "PREPROCESSING IN ATTRACTOR NEURAL NETWORKS." International Journal of Modern Physics C 06, no. 01 (February 1995): 1–10. http://dx.doi.org/10.1142/s0129183195000022.

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Preprocessing the input patterns seems the simplest approach to invariant pattern recognition by neural networks. The Fourier transform has been proposed as an appropriate and elegant preprocessor. Nevertheless, we show in this work that the performance of this kind of preprocessor is strongly affected by the number of stored informations. This is so because the phase of the Fourier transform plays a more important role than the amplitude in the recognition process.

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ZANCHETTIN, CLEBER, LEANDRO L. MINKU, and TERESA B. LUDERMIR. "DESIGN OF EXPERIMENTS IN NEURO-FUZZY SYSTEMS." International Journal of Computational Intelligence and Applications 09, no. 02 (June 2010): 137–52. http://dx.doi.org/10.1142/s1469026810002823.

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Interest in hybrid methods that combine artificial neural networks and fuzzy inference systems has grown in recent years. These systems are robust solutions that search for representations of domain knowledge, reasoning on uncertainty, automatic learning and adaptation. However, the design and definition of the parameter effectiveness of such systems is still a hard task. In the present work, we perform a statistical analysis to verify interactions and interrelations between parameters in the design of neuro-fuzzy systems. The analysis is carried out using a powerful statistical tool, namely, Design of Experiments (DOE), in two neuro-fuzzy models — Adaptive Neuro Fuzzy Inference System (ANFIS) and Evolving Fuzzy Neural Networks (EFuNN). The results show that, for ANFIS, input MFs number and output MFs shape are usually the factors with the largest influence on the system's RMSE. For EFFuNN, the MF shape and the interaction between MF shape and number usually have the largest effect size.

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