Download e-book for kindle: Computational Economics: A Perspective from Computational by Shu-Heng Chen

By Shu-Heng Chen

ISBN-10: 1591406498

ISBN-13: 9781591406495

Chen, Jain, and Tai compile various fascinating functions of computational intelligence methods of their edited Computational Economics: A standpoint from Computational Intelligence booklet. Contributions during this quantity exhibit how mixtures of neural networks, genetic algorithms, wavelets, fuzzy units, and agent-based modeling are used in fixing a number of managerial decision-making difficulties. the amount is wealthy with purposes in monetary modeling, alternative pricing, market-making, optimization of industry concepts, optimization for site visitors coverage, price estimation, coverage appraisal in a legal justice approach, capital keep watch over, and fixing association thought difficulties.

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At the same time, the detailed examination of the performance surface demonstrates that L∞ minimization might be particularly appropriate for multi-objective optimization. A natural path for future work is to apply multi-objective GA for this kind of problem. Having identified valuable relationships between the value of risk aversion coefficient and the order of the loss function, the results presented support ‘active learning’, where the knowledge about the target is gained by some means rather than random sampling.

Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 20 Hayward GA optimization did not identify higher memory depth as optimal for long training periods in comparison to shorter ones. At the same time, the optimal number of hiddenlayer neurons is found to be proportional to the length of training. Thus, longer training produces increased complexity in the relationships, where older data is not necessarily useful for the current/future state modeling and forecasting.

31) In (31) the input layer has I inputs, {ct-0,…, ct-I}; the hidden layer has J hidden nodes and the output layer has one output, Fc(Ct+1). Layers are fully connected by weights, ψi,j; ψo and ψoj are biases. Transfer functions are represented by h1 and h2. Experiments in this chapter are run under two transfer functions, the hyperbolic tangent, 1 2 hs ( x) = − 1 , with –1

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Computational Economics: A Perspective from Computational Intelligence by Shu-Heng Chen


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