Abstract: A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

Abstract: A plausible neural network (PLANN) is an artificial neural network with weight connection given by mutual information, which has the capability of inference and learning, and yet retains many characteristics of a biological neural network. The learning algorithm is based on statistical estimation, which is faster than the gradient decent approach currently used. The network after training becomes a fuzzy/belief network; the inference and weight are exchangeable, and as a result, knowledge extraction becomes simple. PLANN performs associative memory, supervised, semi-supervised, unsupervised learning and function/relation approximation in a single network architecture. This network architecture can easily be implemented by analog VLSI circuit design.

Abstract: An apparatus and method for performing parallel distributed processing are disclosed. A plurality of nodes are connected with weight connections. The weight connections are updated based on a likelihood function of the associated nodes. Also, inputs to nodes are aggregated using t-norm or t-conorm functions, with outputs representing the possibility and belief measures. The aggregation methods presented offer an improvement over many other classification methods. Because of the form of the output, additional data evidence, including additional attributes, may be taken into account to improve classification without retraining the original data.