Advances in Soft Computing: Engineering Design and by Chin-Pao Hung, Mang-Hui Wang (auth.), Jose Manuel Benítez,

By Chin-Pao Hung, Mang-Hui Wang (auth.), Jose Manuel Benítez, Oscar Cordón, Frank Hoffmann, Rajkumar Roy (eds.)

Soft computing embraces methodologies for the advance of clever platforms which have been effectively utilized to a good number of real-word difficulties. This number of keynote papers, provided on the seventh online global convention on tender Computing in Engineering layout and production, presents a complete evaluation of contemporary advances in fuzzy, neural and evolutionary computing innovations and purposes in engineering layout and production.

Features:
- New and hugely complicated study effects on the vanguard of sentimental computing in engineering layout and production.
- Keynote papers by way of world-renowned researchers within the box.
- an exceptional review of present smooth computing study all over the world.

A choice of methodologies geared toward researchers layout and production engineers who boost and follow clever structures in machine engineering.

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E. Hinton. Glove-talk: a neural network interface between a data-glove and a speech synthesizer. IEEE Trans. 2-8, 1993. 5. S. Knerr, L. Personnaz, and G. Dreyfus. Handwritten digit recognition by neural networks with single-layer training. IEEE Trans. on Neural Networks, vol. 3, pp. 962-968, 1992. 6. L. Prechelt. PROBENI - A set of benchmarks and benchmarking rules for neural network training algorithms. Technical Report 21/94, Fakultiit fiir Informatik, Universitiit Karlsruhe, D-76128 Karlsruhe, Germany, September 1994.

Table 1. Comparison of training of connectionist models Network model MLP ERNN RBFN Number of hid- Number of hid- Activation function den neurons den layers used in hidden layer 45 Log-sigmoid I 4S 1 Tan-sigmoid Gaussian 180 2 Activation function used in output layer Pure linear Pure linear Pure linear The optimal network is the one that should have the lowest error on test set and reasonable learning time. All the obtained results were compared and evaluated by the Maximum Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Mean Absolute Deviation (MAD).

Next all weights in the network are retrained. This procedure was repeated until the number of hidden neurons reaches a preset limit and then substantially reduces the training time in comparison with time needed for training of new networks from scratch. More importantly, it creates a nested set of networks having a monotonously decreasing training error and provides some continuity in the model space, which makes a prediction risk minimum more easily noticeable. Due to sudden variation in weather parameters, the model becomes obsolete and inaccurate.

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