Навчання модулів нейронної мережі

Currently, there exists a huge number of neural networks of different classes, each with itsown advantages and disadvantage. However, there aren’t a lot of focus on hybrid neural networks, basedon the combination of knowт topologies of neural networks. Modular organization principle seems to bevery promising, however principles of its module creation isn’t known and needs further research. Thepresent study, therefore, proposes some methods of hybrid neural network module creation and theirlearning algorithmsРассмотрена система гибридных нейронных сетей. Предложены методы обучения их модулейРозглянуто систему гібридних нейронних мереж. Запропоновано методи навчання їх модулі

MATLAB PROGRAM CODES FOR BIDIRECTIONAL ASSOCIATIVE MEMORY NETWORKS

Objective Neural networks are being used for solving problems in various diverse areas including education, research, business, management, and many more. In this article, models describing the dynamics of bidirectional associative memory (BAM) neural networks are considered. Methods: MATLAB, the numerical computing environment and programming language is used for solving certain problems associated with BAM. Results: The concept of BAM networks is improved so that it can be applied to a wider class of networks. Algorithm for solving BAM problems is studied. And also the MATLAB program codes to find the weight matrix, to test the net with input, and to generate activation functions are accompanied. Conclusion: MATLAB programming can be effectively used to solve the problems associated with BAM

Encoding Static and Temporal Patterns with a Bidirectional Heteroassociative Memory

Brain-inspired, artificial neural network approach offers the ability to develop attractors for each pattern if feedback connections are allowed. It also exhibits great stability and adaptability with regards to noise and pattern degradation and can perform generalization tasks. In particular, the Bidirectional Associative Memory (BAM) model has shown great promise for pattern recognition for its capacity to be trained using a supervised or unsupervised scheme. This paper describes such a BAM, one that can encode patterns of real and binary values, perform multistep pattern recognition of variable-size time series and accomplish many-to-one associations. Moreover, it will be shown that the BAM can be generalized to multiple associative memories, and that it can be used to store associations from multiple sources as well. The various behaviors are the result of only topological rearrangements, and the same learning and transmission functions are kept constant throughout the models. Therefore, a consistent architecture is used for different tasks, thereby increasing its practical appeal and modeling importance. Simulations show the BAM's various capacities, by using several types of encoding and recall situations