Multilayer neural network of adaptive bidirectional associative memories |
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| Institution: | 1. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China;2. Key Laboratory of Optimization and Control, Ministry of Education, Chongqing 401331, China;3. Department of Mathematics, College of Mathematics and Computing Science, Changsha University of Science and Technology, Changsha, Hunan 410076, China;1. Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Wanzhou, Chongqing, 404100, China;2. School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China;3. Key Laboratory of Optimization and Control, Ministry of Education, Chongqing 401331, China;4. College of Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China;5. School of Mathematics, Southeast University, Nanjing 210096, China |
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| Abstract: | The analysis of the functioning of the brain allows to propose a computational model of multilayer artificial neural network susceptible of associating some response to a particular input, so that when we present that input, we get the required output by the stability of its states and by minimizing the function of energy of the network. The problem of explosion in the number of interconnections has been solved by the introduction of a layer between the input and the output layer of the network. In this paper, we propose the adaptive bidirectional associative memory by conjugate gradient algorithm, so as to study the behavior and performances of the network on pairs of patterns through using the autoassociative or heteroassociative memories. |
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