Neural network system with sampling data

摘要

A neural network system includes input, intermediate and output layers, each layer containing at least one neural network element, each having an input and output, for simulating a neuron; and a plurality of inter-layer connections between neural elements wherein each input layer element has a connection to at least one intermediate layer element, and each intermediate layer element has a connection to at least one output layer element. Each inter-layer connection has a connecting weight. The system further includes sampling data and teaming data. The sampling data has pairs of values, each pair including an input value and corresponding output value, the input value having regular intervals. The learning data has at least three pairs of values, each pair including an input value and corresponding desired output value, the input values having irregular intervals. The intermediate layer elements are assigned unique sampling data value pairs and have unique sampling functions derived by translating original sampling functions by sampling data input values assigned to the neural elements. The sampling function defines a relationship between the input and output of the neural element. The connecting weight for each connection between an intermediate layer element and an output layer element is set to the sampling data output value assigned to the intermediate layer element. The system further includes a training mechanism that adjusts the connecting weights to minimize errors between learning data output values and actual output values obtained by applying the learning data input values to the neural network.