A Neural Net Model for Prediction
提出一种基于贝叶斯推断和卡尔曼滤波的随机神经网络模型，用于非线性统计预测，在噪声混沌时间序列预测中比牛顿法更准确。
Abstract In this article we introduce a neural net designed for nonlinear statistical prediction. The net is based on a stochastic model featuring a multilayer feedforward architecture with random connections between units and noisy response functions. A Bayesian inferential procedure for this model, based on the Kalman filter, is derived. The resulting learning algorithm generalizes the so-called onedimensional Newton method, an updating algorithm currently popular in the neural net literature. A numerical study concerning the prediction of a noisy chaotic time series is presented, and the greater predictive accuracy of the new algorithm with respect to the Newton algorithm is exhibited.