Abstract: A neural network system includes an input unit, an operation control unit, a parameter setting unit, a neural network group unit, and a display unit. The network group unit includes first and second neural networks. The first neural network operates according to the mean field approximation method to which the annealing is added, whereas the second neural network operates in accordance with the simulated annealing. Each of the first an second neural networks includes a plurality of neurons each connected via synapses to neurons so as to weighting outputs from the neurons based on synapse weights, thereby computing an output related to a total of weighted outputs from the neurons according to an output function. The parameter setting unit is responsive to a setting instruction to generate neuron parameters including synapse weights, threshold values, and output functions, which are set to the first neural network and which are selective set to the second neural network.

Abstract: A neural network system capable of performing integrated processing of a plurality of information includes a feature extractor group for extracting a plurality of learning feature data from learning data in a learning mode and a plurality of object feature data from object data to be processed in an execution mode, and an information processing unit for learning features of the learning data, based on the plurality of learning feature data from the feature extractor group and corresponding teacher data in the learning mode, and determining final learning result data from the plurality of object feature data from the feature extractor group in accordance with the learning result, including a logic representing relation between the plurality of object feature data in the execution mode.

Abstract: In order to improve the problematical points concerning the structure and the processing speed of a prior art neural network, the optimum structure of the neural network, in which a synapse structure constructed on the basis of living body physiological knowledge or presumed therefrom is determined to make it possible to realize high level information processing functions such as feature extraction, feature unification, memory, etc. Applications to an image recognition, a movement control, etc. making the most of the robust recognizing power thereof, or application to an optimum problem, a large scale numerical analysis, etc. making the most of the parallel processing power thereof are made possible.

Abstract: Method and system of selecting desirable time series data pieces from many time series data pieces. Characteristics extracted from time series data pieces are applied to neurons and neuron state fluctuations are supervised and evaluated through execution of neurocomputing to select desirable time series data.

Abstract: An image data processing method for restoring an image worsened by non-linear characteristics of a transforming system, noises and others is disclosed, by which a quantity called energy function, in which the noises and others described above are taken into consideration, is defined, and treatments for modifying stochastically the value of the gray level of each of the pixels are repeated so that a probability determined by this energy function is made greatest and the function E({x.sub.i,j }) represented by the following equation is adopted for the energy function stated above; ##EQU1## where x.sub.k,l is the value of the gray level of the group of pixels directly adjacent to the sides of the object pixel x.sub.i,j ; y.sub.i,j is the value of the gray level at that time of the object pixel; f(x.sub.i,j) is the value corresponding to the true gray level of the object pixel obtained through the transformation system for transforming image signals into a first image data; and .alpha. is a coefficient.