By B. Yegnanarayana
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Additional info for Artificial Neural Networks
7 Summary In this chapter we have seen the motivation and background for the current interest in the study of problems based on models using artificial neural networks. We have reviewed the features of the biological neural network and discussed the feasibility of realizing Review Questions 37 some of these features through parallel and distributed processing (PDP) models (Appendix A). In particular, the associative memory, fault tolerance and concept learning features could be demonstrated through these PDP models.
Use suitable values for the initial weights. ) Using the Instar learning law, group all the sixteen possible binary vectors of length 4 into four different groups. Use suitable values for the initial weights and for the learning rate parameter. Use a 4-unit input and 4-unit output network. Select random initial weights in the range [O, 11. 1 Introduction An artificial neural network consists of several processing units (or artificial neurons) interconnected in a predetermined manner to accomplish a desired pattern recognition task.
BMITis the desired response from the layer of M units. The outstar learning is a supervised learning law, and it is used with a network of instars to capture the characteristics of the input and output patterns for data compression. In ,implementation, the weight vectors are initialized to zero prior t o blaming. 2 Summary of Basic Learning Laws (Adapted from [Zurada, 19921) Learning law Hebbian Weight adjustment Initial hU, weights Ta) a, h.. , M Random but Unsupervised n~rmalised Zero Supervised Basics of Artificial Neural Networks far.