A new bias method for the optical implementation of cellular neural networks is proposed to reduce electronic precalculation and increase processing speed. A multiple-object joint transform correlator is then used to realize the summation of multiple correlations resulting from the bias method. Compared with other optical systems for cellular neural networks, the proposed method offers the advantages of higher processing speed, easy implementation, and robustness. Computer simulations of the optical cellular neural networks for edge detection and corner and horizontal line extraction are also presented.

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A new optoelectronic fuzzy inference system is proposed for processing a large number of fuzzy rules in parallel. The proposed system using spatial light modulator implements various membership functions as well as max-mm inference. It has the features of easy implementation and large data processing capability. The membership function decomposition method in the improved fuzzy associative memory is used to save both space bandwidth and accommodate multiple-input fuzzy inference.

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A fuzzy winner-take-all model is proposed for performing fuzzy logic inference. An optoelectronic scheme for this model is presented, and experimental results are given.