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Journal articles on the topic 'Neural networs'

Author: Grafiati

Published: 28 May 2022

Last updated: 18 July 2025

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1

Kaur, Amritpal, and Dr Yogeshwar Randhawa. "Image Segmentation with Artificial Neural Networs Alongwith Updated Jseg Algorithm." IOSR Journal of Electronics and Communication Engineering 9, no. 4 (2014): 01–13. http://dx.doi.org/10.9790/2834-09420113.

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2

Navghare, Tukaram, Aniket Muley, and Vinayak Jadhav. "Siamese Neural Networks for Kinship Prediction: A Deep Convolutional Neural Network Approach." Indian Journal Of Science And Technology 17, no. 4 (2024): 352–58. http://dx.doi.org/10.17485/ijst/v17i4.3018.

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3

O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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4

Boonsatit, Nattakan, Santhakumari Rajendran, Chee Peng Lim, Anuwat Jirawattanapanit, and Praneesh Mohandas. "New Adaptive Finite-Time Cluster Synchronization of Neutral-Type Complex-Valued Coupled Neural Networks with Mixed Time Delays." Fractal and Fractional 6, no. 9 (2022): 515. http://dx.doi.org/10.3390/fractalfract6090515.

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The issue of adaptive finite-time cluster synchronization corresponding to neutral-type coupled complex-valued neural networks with mixed delays is examined in this research. A neutral-type coupled complex-valued neural network with mixed delays is more general than that of a traditional neural network, since it considers distributed delays, state delays and coupling delays. In this research, a new adaptive control technique is developed to synchronize neutral-type coupled complex-valued neural networks with mixed delays in finite time. To stabilize the resulting closed-loop system, the Lyapun

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5

Mahat, Norpah, Nor Idayunie Nording, Jasmani Bidin, Suzanawati Abu Hasan, and Teoh Yeong Kin. "Artificial Neural Network (ANN) to Predict Mathematics Students’ Performance." Journal of Computing Research and Innovation 7, no. 1 (2022): 29–38. http://dx.doi.org/10.24191/jcrinn.v7i1.264.

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Predicting students’ academic performance is very essential to produce high-quality students. The main goal is to continuously help students to increase their ability in the learning process and to help educators as well in improving their teaching skills. Therefore, this study was conducted to predict mathematics students’ performance using Artificial Neural Network (ANN). The secondary data from 382 mathematics students from UCI Machine Learning Repository Data Sets used to train the neural networks. The neural network model built using nntool. Two inputs are used which are the first and the

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Jiang, Yiming, Chenguang Yang, Shi-lu Dai, and Beibei Ren. "Deterministic learning enhanced neutral network control of unmanned helicopter." International Journal of Advanced Robotic Systems 13, no. 6 (2016): 172988141667111. http://dx.doi.org/10.1177/1729881416671118.

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In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid wit

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Zengguo Sun, Zengguo Sun, Guodong Zhao Zengguo Sun, Rafał Scherer Guodong Zhao, Wei Wei Rafał Scherer, and Marcin Woźniak Wei Wei. "Overview of Capsule Neural Networks." 網際網路技術學刊 23, no. 1 (2022): 033–44. http://dx.doi.org/10.53106/160792642022012301004.

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<p>As a vector transmission network structure, the capsule neural network has been one of the research hotspots in deep learning since it was proposed in 2017. In this paper, the latest research progress of capsule networks is analyzed and summarized. Firstly, we summarize the shortcomings of convolutional neural networks and introduce the basic concept of capsule network. Secondly, we analyze and summarize the improvements in the dynamic routing mechanism and network structure of the capsule network in recent years and the combination of the capsule network with other network structures

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N, Vikram. "Artificial Neural Networks." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4308–9. http://dx.doi.org/10.55248/gengpi.4.423.37858.

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9

Murugan, S., and Dr M. Jeyakarthic. "Optimal Deep Neural Network based Classification Model for Intrusion Detection in Mobile Adhoc Networks." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1374–87. http://dx.doi.org/10.5373/jardcs/v11sp10/20192983.

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10

Botmart, T., N. Yotha, K. Mukdasai, and W. Weera. "Improved Results on Passivity Analysis of Neutral-Type Neural Networks with Mixed Time-Varying Delays." International Journal of Information and Electronics Engineering 8, no. 3 (2018): 30–35. http://dx.doi.org/10.18178/ijiee.2018.8.3.690.

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11

Zhou, Wuneng, Xueqing Yang, Jun Yang, and Jun Zhou. "Stochastic Synchronization of Neutral-Type Neural Networks with Multidelays Based onM-Matrix." Discrete Dynamics in Nature and Society 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/826810.

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The problem of stochastic synchronization of neutral-type neural networks with multidelays based onM-matrix is researched. Firstly, we designed a control law of stochastic synchronization of the neural-type and multiple time-delays neural network. Secondly, by making use of Lyapunov functional andM-matrix method, we obtained a criterion under which the drive and response neutral-type multiple time-delays neural networks with stochastic disturbance and Markovian switching are stochastic synchronization. The synchronization condition is expressed as linear matrix inequality which can be easily s

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12

Faydasicok, Ozlem. "New Results on Stability of Delayed Cohen–Grossberg Neural Networks of Neutral Type." Complexity 2020 (June 16, 2020): 1–10. http://dx.doi.org/10.1155/2020/1973548.

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This research work conducts an investigation of the stability issues of neutral-type Cohen–Grossberg neural network models possessing discrete time delays in states and discrete neutral delays in time derivatives of neuron states. By setting a new generalized appropriate Lyapunov functional candidate, some novel sufficient conditions are proposed for global asymptotic stability for the considered neural networks of neutral type. This paper exploits some basic properties of matrices in the derivation of the results that establish a set of algebraic mathematical relationships between network par

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13

Hameed, Maysaa, Mahmood Zaki Abdullah, Ali Khalid Jassim, and Mohammed Majid M. Al Khalidy. "A Hybrid for Analyzing Text Streaming Using Data Mining and Machine Learning Techniques." Journal of Engineering and Sustainable Development 28, no. 5 (2024): 675–80. http://dx.doi.org/10.31272/jeasd.28.5.13.

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Human opinions and feelings can be studied and analyzed in various fields. Sentiment analysis divides data into neutral, positive, and negative categories to classify a writer's or speaker's attitude toward various topics or events. This study uses a hybrid approach that combines (Particle_Swarm_Optimization PSO) with machine learning classifiers (Artificial Neural_ Networks ANN, Naïve Base NB, and Support.Vector.Machine SVM). Following the preprocessing phase of each data collection, a Convolution Neural Network (CNN) will be used to create feature vectors, preparing the raw data as a stored

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14

Gong, Xiao Lu, Zhi Jian Hu, Meng Lin Zhang, and He Wang. "Wind Power Forecasting Using Wavelet Decomposition and Elman Neural Network." Advanced Materials Research 608-609 (December 2012): 628–32. http://dx.doi.org/10.4028/www.scientific.net/amr.608-609.628.

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The relevant data sequences provided by numerical weather prediction are decomposed into different frequency bands by using the wavelet decomposition for wind power forecasting. The Elman neural network models are established at different frequency bands respectively, then the output of different networks are combined to get the eventual prediction result. For comparison, Elman neutral network and BP neutral network are used to predict wind power directly. Several error indicators are given to evaluate prediction results of the three methods. The simulation results show that the Elman neural n

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15

Kosterin, Maksim A., and Ilya V. Paramonov. "Neural Network-Based Sentiment Classification of Russian Sentences into Four Classes." Modeling and Analysis of Information Systems 29, no. 2 (2022): 116–33. http://dx.doi.org/10.18255/1818-1015-2022-2-116-133.

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The paper is devoted to the classification of Russian sentences into four classes: positive, negative, mixed, and neutral. Unlike the majority of modern study in this area, the mixed sentiment class is introduced. Mixed sentiment sentences contain positive and negative sentiments simultaneously.To solve the problem, the following tools were applied: the attention-based LSTM neural network, the dual attention-based GRU neural network, the BERT neural network with several modifications of the output layer to provide classification into four classes. The experimental comparison of the efficiency

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16

Perfetti, R. "A neural network to design neural networks." IEEE Transactions on Circuits and Systems 38, no. 9 (1991): 1099–103. http://dx.doi.org/10.1109/31.83884.

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17

Tran, Loc. "Directed Hypergraph Neural Network." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1434–41. http://dx.doi.org/10.5373/jardcs/v12sp4/20201622.

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18

Antipova, E. S., and S. A. Rashkovskiy. "Autoassociative Hamming Neural Network." Nelineinaya Dinamika 17, no. 2 (2021): 175–93. http://dx.doi.org/10.20537/nd210204.

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An autoassociative neural network is suggested which is based on the calculation of Hamming distances, while the principle of its operation is similar to that of the Hopfield neural network. Using standard patterns as an example, we compare the efficiency of pattern recognition for the autoassociative Hamming network and the Hopfield network. It is shown that the autoassociative Hamming network successfully recognizes standard patterns with a degree of distortion up to $40\%$ and more than $60\%$, while the Hopfield network ceases to recognize the same patterns with a degree of distortion of m

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19

Василенко, Н. Е., та Н. А. Медведева. "Нейросетевая система видеонаблюдения". Proceedings in Cybernetics 23, № 4 (2024): 25–33. https://doi.org/10.35266/1999-7604-2024-4-3.

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The purpose of the paper is to design and implement an effi cient and affordable automatic surveillance system, which can operate in real time and integrate with existing surveillance systems. The method presented includes analyzing existing solutions on the market, selecting and training a profound learning model for objects detection, developing user interfaces, containerizing of the application and testing the system in a real-world environment. The result is a system capable of detecting objects of interest in real time using neural networks and notifying the user of the detected items. Th

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20

Miao, Lu, Wei Fan, Yu Liu, Yingjie Qin, Deyang Chen, and Jiayan Cui. "Optimization of PSO-BP neural network for short-term wind power prediction." International Journal of Low-Carbon Technologies 19 (2024): 2687–92. http://dx.doi.org/10.1093/ijlct/ctae234.

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Abstract This paper uses a back propagation (BP) neural network to predict short-term wind power. Since the initial weights and thresholds of BP neural networks significantly impact their performance, we use the optimized particle swarm optimization (PSO) to obtain the critical parameters of BP neural networks. Specifically, we optimize the PSO to make it easier to get better parameters. The experimental results show that the BP neural network's mean relative error (MRE) is 11.91%, 15.18%, and 8.56%, respectively. In comparison, the MRE of the optimized BP neural network is 5.09%, 7.21%, and 4

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21

Ma, Yunlong, Tao Xie, and Yijia Zhang. "Robustness analysis of neutral fuzzy cellular neural networks with stochastic disturbances and time delays." AIMS Mathematics 9, no. 10 (2024): 29556–72. http://dx.doi.org/10.3934/math.20241431.

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<p>This paper discusses the robustness of neutral fuzzy cellular neural networks with stochastic disturbances and time delays. This work questions whether fuzzy cellular neural networks, which initially remains stable, can be stabilised again when the system is subjected to three simultasneous perturbations i.e., neutral items, random disturbances, and time delays. First, by using inequality techniques such as Gronwall's Lemma, the Itŏ formula, and the property of integrals, the transcendental equations that contain the contraction coefficient of the neutral terms, the intensity of the r

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22

Li, Yongkun, and Xiaofang Meng. "Existence and Global Exponential Stability of Pseudo Almost Periodic Solutions for Neutral Type Quaternion-Valued Neural Networks with Delays in the Leakage Term on Time Scales." Complexity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9878369.

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We propose a class of neutral type quaternion-valued neural networks with delays in the leakage term on time scales that can unify the discrete-time and the continuous-time neural networks. In order to avoid the difficulty brought by the noncommutativity of quaternion multiplication, we first decompose the quaternion-valued system into four real-valued systems. Then, by applying the exponential dichotomic theory of linear dynamic equations on time scales, Banach’s fixed point theorem, the theory of calculus on time scales, and inequality techniques, we obtain some sufficient conditions on the

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23

Pan, Tetie, Bao Shi, and Jian Yuan. "Global Stability of Almost Periodic Solution of a Class of Neutral-Type BAM Neural Networks." Abstract and Applied Analysis 2012 (2012): 1–18. http://dx.doi.org/10.1155/2012/482584.

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A class of BAM neural networks with variable coefficients and neutral delays are investigated. By employing fixed-point theorem, the exponential dichotomy, and differential inequality techniques, we obtain some sufficient conditions to insure the existence and globally exponential stability of almost periodic solution. This is the first time to investigate the almost periodic solution of the BAM neutral neural network and the results of this paper are new, and they extend previously known results.

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24

Yagup, Kateryna, and Valery Yagup. "APPLICATION OF A NEURONETWORK FOR DETERMINING THE TYPE OF ELEMENTS OF A SYMMETRICAL COMPENSATION DEVICE OF AN UNSYMMETRICAL SYSTEM WITH A ZERO WIRE." Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, no. 1 (11) (July 30, 2024): 76–79. http://dx.doi.org/10.20998/2079-0023.2024.01.12.

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In article the possibility of using neural networks in the field of increasing the energy performance of a four-wire power supply system with an uneven load in the phases is being investigated. An uneven load in the phases causes asymmetry of currents in the network and contributes to the increase in the current in the neutral wire, which has an extremely negative effect on both the supply itself and its consumers. To eliminate asymmetry and reduce the current in the neutral wire, you can connect a symmetrical compensating device. Such a device is a set of reactive elements, the parameters of

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25

D, Sreekanth. "Metro Water Fraudulent Prediction in Houses Using Convolutional Neural Network and Recurrent Neural Network." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 1177–87. http://dx.doi.org/10.47059/revistageintec.v11i4.2177.

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26

Azlah, Muhammad Azfar Firdaus, Lee Suan Chua, Fakhrul Razan Rahmad, Farah Izana Abdullah, and Sharifah Rafidah Wan Alwi. "Review on Techniques for Plant Leaf Classification and Recognition." Computers 8, no. 4 (2019): 77. http://dx.doi.org/10.3390/computers8040077.

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Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extract

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27

Kurniawati, Ika. "Deep Learning Model Based on Particle Swam Optimization for Buzzer Detection." Journal of Informatics Information System Software Engineering and Applications (INISTA) 7, no. 1 (2024): 22–32. https://doi.org/10.20895/inista.v7i1.1622.

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Along with the development of the internet, the presence of buzzers is increasingly widespread on social platforms, especially on Twitter. Buzzers have played an important role in influencing and spreading misinformation, manipulating public opinion, and harassing and intimidating online social media users. Therefore, an effective detection algorithm is needed to detect buzzer accounts that endanger social networks because they affect neutrality. In this research, we propose a Deep Neural Network model to detect buzzer accounts on Twitter. We conducted experiments on 1000 datasets using PSO-ba

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28

SAKTHIVEL, RATHINASAMY, R. SAMIDURAI, and S. MARSHAL ANTHONI. "EXPONENTIAL STABILITY FOR STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE WITH IMPULSIVE EFFECTS." Modern Physics Letters B 24, no. 11 (2010): 1099–110. http://dx.doi.org/10.1142/s0217984910023141.

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This paper is concerned with the exponential stability of stochastic neural networks of neutral type with impulsive effects. By employing the Lyapunov functional and stochastic analysis, a new stability criterion for the stochastic neural network is derived in terms of linear matrix inequality. A numerical example is provided to show the effectiveness and applicability of the obtained result.

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29

PARK, JU H., and O. M. KWON. "SYNCHRONIZATION OF NEURAL NETWORKS OF NEUTRAL TYPE WITH STOCHASTIC PERTURBATION." Modern Physics Letters B 23, no. 14 (2009): 1743–51. http://dx.doi.org/10.1142/s0217984909019909.

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In this letter, the problem of feedback controller design to achieve synchronization for neural network of neutral type with stochastic perturbation is considered. Based on Lyapunov method and LMI (linear matrix inequality) framework, the goal of this letter is to derive an existence criterion of the controller for the synchronization between master and response networks.

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30

Suk-Hwan, Jung, and Chung Yong-Joo. "Sound event detection using deep neural networks." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 5 (2020): 2587~2596. https://doi.org/10.12928/TELKOMNIKA.v18i5.14246.

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We applied various architectures of deep neural networks for sound event detection and compared their performance using two different datasets. Feed forward neural network (FNN), convolutional neural network (CNN), recurrent neural network (RNN) and convolutional recurrent neural network (CRNN) were implemented using hyper-parameters optimized for each architecture and dataset. The results show that the performance of deep neural networks varied significantly depending on the learning rate, which can be optimized by conducting a series of experiments on the validation data over predetermined r

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31

Kadhim, Mohammed Aboud, Sadeer Rasheed Ahmed, and Ahmed Rifaat Hamad. "Intelligent traffic load optimization and channel allocation in next-generation wireless networks using neural networks." Edelweiss Applied Science and Technology 9, no. 5 (2025): 740–56. https://doi.org/10.55214/25768484.v9i5.7001.

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We explore neural network-based optimization techniques for resource allocation and management in dense wireless networks in the research. The growing demand for efficient communication in contemporary wireless systems makes the isolation of traffic load and channel distribution essential for guaranteeing the best possible development of the network. The new approach is based on artificial neural networks and proactively assigns the available frequency-related channels to users according to their traffic load. The neural network is trained on the data to predict the optimal channel allocation

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32

Dr., Rakesh Kumar Bhujade, and Stuti Asthana Dr. "An Novel Approach on the Number of Hidden Nodes Optimizing in Artificial Neural Network." International Journal of Applied Engineering & Technology 4, no. 2 (2022): 106–9. https://doi.org/10.5281/zenodo.7413107.

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Forecasting, classification, and data analysis may all gain from improved pattern recognition results. Neural Networks are very effective and adaptable for pattern recognition and a variety of other real-world problems, such as signal processing and classification concerns. Providing improved pattern recognition results for predicting, categorizing, and data analysis. To provide correct results, neural networks need sufficient data pre-processing, architecture selection, and network training; nevertheless, the performance of a neural network is reliant on the size of its network. The correct p

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33

Yen, Gary G., and Haiming Lu. "Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design." International Journal of Computational Intelligence and Applications 03, no. 03 (2003): 213–32. http://dx.doi.org/10.1142/s1469026803000975.

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In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the traini

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34

Feng, Wei, Simon X. Yang, and Haixia Wu. "Robust Stability Analysis of Neutral-Type Hybrid Bidirectional Associative Memory Neural Networks with Time-Varying Delays." Abstract and Applied Analysis 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/560861.

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The global asymptotic robust stability of equilibrium is considered for neutral-type hybrid bidirectional associative memory neural networks with time-varying delays and parameters uncertainties. The results we obtained in this paper are delay-derivative-dependent and establish various relationships between the network parameters only. Therefore, the results of this paper are applicable to a larger class of neural networks and can be easily verified when compared with the previously reported literature results. Two numerical examples are illustrated to verify our results.

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35

FUKUSHIMA, Kunihiko. "Neocognitron: Deep Convolutional Neural Network." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 27, no. 4 (2015): 115–25. http://dx.doi.org/10.3156/jsoft.27.4_115.

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36

AVeselý. "Neural networks in data mining." Agricultural Economics (Zemědělská ekonomika) 49, No. 9 (2012): 427–31. http://dx.doi.org/10.17221/5427-agricecon.

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To posses relevant information is an inevitable condition for successful enterprising in modern business. Information could be parted to data and knowledge. How to gather, store and retrieve data is studied in database theory. In the knowledge engineering, there is in the centre of interest the knowledge and methods of its formalization and gaining are studied. Knowledge could be gained from experts, specialists in the area of interest, or it can be gained by induction from sets of data. Automatic induction of knowledge from data sets, usually stored in large databases, is called data mining.

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J, Joselin, Dinesh T, and Ashiq M. "A Review on Neural Networks." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 565–69. http://dx.doi.org/10.31142/ijtsrd18461.

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CVS, Rajesh, and Nadikoppula Pardhasaradhi. "Analysis of Artificial Neural-Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (2018): 418–28. http://dx.doi.org/10.31142/ijtsrd18482.

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Alle, Kailash. "Sentiment Analysis Using Neural Networks." International Journal of Science and Research (IJSR) 7, no. 12 (2018): 1604–8. http://dx.doi.org/10.21275/sr24716104045.

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Gumen, O., I. Selina, and D. Міz. "NEURAL NETWORKS. COMPUTER VISUAL RECOGNITION." Modern problems of modeling, no. 26 (June 13, 2024): 95–99. https://doi.org/10.33842/2313125x-2024-26-95-99.

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Ziroyan, M. A., E. A. Tusova, A. S. Hovakimian, and S. G. Sargsyan. "Neural networks apparatus in biometrics." Contemporary problems of social work 1, no. 2 (2015): 129–37. http://dx.doi.org/10.17922/2412-5466-2015-1-2-129-137.

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R, Adarsh, and Dr Suma. "Neural Network for Financial Forecasting." International Journal of Research Publication and Reviews 5, no. 5 (2024): 13455–58. http://dx.doi.org/10.55248/gengpi.5.0524.1476.

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Shchetinin, E. Yu. "EMOTIONS RECOGNITION IN HUMAN SPEECH USING DEEP NEURAL NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 199 (January 2021): 44–51. http://dx.doi.org/10.14489/vkit.2021.01.pp.044-051.

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The recognition of human emotions is one of the most relevant and dynamically developing areas of modern speech technologies, and the recognition of emotions in speech (RER) is the most demanded part of them. In this paper, we propose a computer model of emotion recognition based on an ensemble of bidirectional recurrent neural network with LSTM memory cell and deep convolutional neural network ResNet18. In this paper, computer studies of the RAVDESS database containing emotional speech of a person are carried out. RAVDESS-a data set containing 7356 files. Entries contain the following emotion

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D., Geraldine Bessie Amali, and M. Dinakaran. "A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 1 (2017): 26–32. https://doi.org/10.5281/zenodo.4108225.

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Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorith

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YEN, GARY, and HAIMING LU. "HIERARCHICAL GENETIC ALGORITHM FOR NEAR-OPTIMAL FEEDFORWARD NEURAL NETWORK DESIGN." International Journal of Neural Systems 12, no. 01 (2002): 31–43. http://dx.doi.org/10.1142/s0129065702001023.

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In this paper, we propose a genetic algorithm based design procedure for a multi-layer feed-forward neural network. A hierarchical genetic algorithm is used to evolve both the neural network's topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi-objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey–Glass chaotic ti

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PARK, JU H., and O. M. KWON. "ANALYSIS ON GLOBAL STABILITY OF STOCHASTIC NEURAL NETWORKS OF NEUTRAL TYPE." Modern Physics Letters B 22, no. 32 (2008): 3159–70. http://dx.doi.org/10.1142/s0217984908017680.

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In this paper, the problem of global asymptotic stability of stochastic neural networks of neutral type is considered. Based on the Lyapunov stability theory, a new delay-dependent stability criterion for the network is derived in terms of LMI (linear matrix inequality). A numerical example is given to show the effectiveness of the proposed method.

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Rajesh, CVS. "Basics and Features of Artificial Neural Networks." International Journal of Trend in Scientific Research and Development 2, no. 2 (2018): 1065–69. https://doi.org/10.31142/ijtsrd9578.

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The models of the computing for the perform the pattern recognition methods by the performance and the structure of the biological neural network. A network consists of computing units which can display the features of the biological network. In this paper, the features of the neural network that motivate the study of the neural computing are discussed and the differences in processing by the brain and a computer presented, historical development of neural network principle, artificial neural network ANN terminology, neuron models and topology are discussed. Rajesh CVS | M. Padmanabham "B

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Xu, Xing Mei, Li Ying Cao, and Jing Zhou. "Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network." Applied Mechanics and Materials 713-715 (January 2015): 1939–42. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1939.

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Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the in

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Setiono, Rudy. "Feedforward Neural Network Construction Using Cross Validation." Neural Computation 13, no. 12 (2001): 2865–77. http://dx.doi.org/10.1162/089976601317098565.

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This article presents an algorithm that constructs feedforward neural networks with a single hidden layer for pattern classification. The algorithm starts with a small number of hidden units in the network and adds more hidden units as needed to improve the network's predictive accuracy. To determine when to stop adding new hidden units, the algorithm makes use of a subset of the available training samples for cross validation. New hidden units are added to the network only if they improve the classification accuracy of the network on the training samples and on the cross-validation samples. E

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Yashchenko, V. O. "Neural-like growing networks in the development of general intelligence. Neural-like growing networks (P. II)." Mathematical machines and systems 1 (2023): 3–29. http://dx.doi.org/10.34121/1028-9763-2023-1-3-29.

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This article is devoted to the development of general artificial intelligence (AGI) based on a new type of neural networks – “neural-like growing networks”. It consists of two parts. The first one was published in N4, 2022, and describes an artificial neural-like element (artificial neuron) in terms of its functionality, which is as close as possible to a biological neuron. An artificial neural-like element is the main element in building neural-like growing networks. The second part deals with the structures and functions of artificial and natural neural networks. The paper proposes a new app

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