Applied Intelligent Systems: New Directions by David Edelman, Pam Davy (auth.), Professor John Fulcher,

By David Edelman, Pam Davy (auth.), Professor John Fulcher, Professor Lakhmi C. Jain (eds.)

This rigorously edited publication offers examples of the winning software of clever structures options to sensible difficulties. The invited contributions, written by means of foreign specialists of their respective fields, sincerely reveal what should be completed while AI platforms are used to unravel real-world difficulties. The e-book covers the sector of utilized clever structures with a vast and deep choice of issues, similar to item attractiveness, robotics, satellite tv for pc climate prediction, or economics with an commercial concentration. This publication should be of curiosity to researchers attracted to utilized clever systems/AI, in addition to to engineers and programmers in undefined.

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12) 2. 17) j 1 Combining Eqs. 18) For a linear output neuron, this becomes, by combining Eqs. 19) ( y pk  o pk )(1)i pj ( y pk  o pk )i pj Alternatively, for a sigmoid output neuron, by combining Eqs. 20) ( y pk  o pk )o pk (1 o pk )i pj The weight update equations are formulated as follows: 1. 21) Combining Eqs. 22) o wkj (t) K( y pk  o pk )i pj o ol wkj (t) KG pk i pj 2. for sigmoid output neurons, let: Gospk = (ypk - opk) fok'(netopk) = (ypk - opk) opk(1 - opk) Combining Eqs. 35) i 1 Combining Eqs.

Inui T, Tanabe Y and Onodera Y (1978) Group Theory and its Application in Physics, Springer Verlag, Berlin. Karayiannis N and Venetsanopoulos A (1993) Artificial Neural Networks: Learning Algorithms, Performance Evaluation and Applications, Kluwer, New York (Chapter 7). Lee RST and Liu JNK (2000) Teaching and Learning AI Modeling, in Jain lC (ed) Innovative Teaching and Learning: Knowledge-Based Paradigms, Physica Verlag, New York: 31-86 (Chapter 3). Leshno M, Lin V, Pinkus A and Schoken S (1993) Multi-layer feed-forward networks with a non-polynomial activation can approximate any function, Neural Networks, 6: 861-867.

5. 50) Now since NAHONG comprises Artificial Neural Networks, we can infer the following from [22]: “Consider a Neuron-Adaptive Higher Order Neural Network Group (NAHONG), in which each element is a standard multi-layer higher order neural network with adaptive neurons, and which has locally bounded, piecewise continuous (rather than polynomial) activation function and threshold. Each such group can approximate any kind of piecewise continuous function, and to any degree of accuracy”. A detailed proof is provided in [55].

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