论文信息 - A new supervised learning algorithm for multilayered and interconnected neural networks

A new supervised learning algorithm for multilayered and interconnected neural networks

A new learning algorithm is presented for supervised learning of multilayered and interconnected neural networks without using a gradient method. First, fictitious teacher signals for the outputs of each hidden unit are algebraically determined by an error backpropagation (EBP) method. Then, the weight parameters are determined by using an exponentially weighted least squares (EWLS) method. This is called the EBP-EWLS algorithm for a multilayered neural network. For an interconnected neural network, the mathematical description of the neural network is arranged in the form for which the EBP-EWLS algorithm can be applied. Simulation studies have verified the proposed technique.

Peter N. Nikiforuk | Yoshihiro Yamamoto

[1] Karl Johan Åström,et al. Adaptive Control , 1989, Embedded Digital Control with Microcontrollers.

[2] Peter N. Nikiforuk,et al. On the Generalized Truncated Least Squares Adaptive Algorithm and Two-Stage Design Method with Application to Adaptive Control , 1993 .

[3] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[4] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[5] Chuanyi Ji,et al. Fast training of recurrent networks based on the EM algorithm , 1998, IEEE Trans. Neural Networks.

[6] Chuanyi Ji,et al. A unified approach on fast training of feedforward and recurrent networks using EM algorithm , 1998, IEEE Trans. Signal Process..