Neural Networks and Micromechanics by Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch

By Ernst Kussul, Tatiana Baidyk, Donald C. Wunsch

Micromechanical production in response to microequipment creates new possibi- ties in items creation. If microequipment sizes are equivalent to the sizes of the microdevices to be produced, it really is attainable to diminish the price of creation significantly. the most parts of the construction fee - fabric, power, area intake, gear, and upkeep - lessen with the thinning out of apparatus sizes. to procure particularly reasonably cheap construction, exertions expenses has to be decreased to just about 0. For this objective, totally automatic microfactories might be constructed. To create totally automatic microfactories, we suggest utilizing arti?cial neural networks having diverse constructions. the best perceptron-like neural community can be utilized on the lowest degrees of microfactory regulate structures. Adaptive Critic layout, in line with neural community types of the microfactory items, can be utilized for production strategy optimization, whereas associative-projective neural n- works and networks like paintings can be used for the top degrees of keep watch over platforms. we have now tested the functionality of alternative neural networks in conventional picture popularity initiatives and in difficulties that seem in micromechanical production. We and our colleagues even have built an method of mic- apparatus construction within the kind of sequential generations. every one next gene- tion has to be of a smaller measurement than the former ones and has to be made through earlier generations. Prototypes of ?rst-generation microequipment were constructed and assessed.

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Since there are many blocks in RTC (1, . , s), the whole feature space will be covered by many rectangles of different sizes and locations (Fig. 4 ). In multidimensional space, instead of rectangles we will get multidimensional parallelepipeds. Each parallelepiped corresponds to the active state of one bj neuron (Fig. 2). For example, if we want to recognize the new point (X1*, X2*) in the space of two features (X1 and X2) containing two classes (1 and 2) (Fig. 5), we will obtain the neuron responses which are related to the rectangles that cover the new point.

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