The adaptation of individual learning rates is important for many learning tasks, particularly in the case of nonstationary learning environments. Sutton has presented with the Incremental Delta Bar Delta algorithm a versatile method for many tasks. However, this algorithm was formulated only for linear models. A straightforward generalization to nonlinear models is possible, but we show in thi...

Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system—the LMS or delta rule with a separate learning-rate parameter for each inp...

Appropriate bias is widely viewed as the key to eecient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system|the LMS or delta rule with a separate learning-rate parameter for each input...

The backpropagation learning algorithm for feedforward networks (Rumelhart et al. 1986) has recently been generalized to recurrent networks (Pineda 1989). The algorithm has been further generalized by Pearlmutter (1989) to recurrent networks that produce time-dependent trajectories. The latter method requires much more training time than the feedforward or static recurrent algorithms. Furthermo...

Appropriate bias is widely viewed as the key to e cient learning and generalization I present a new algorithm the Incremental Delta Bar Delta IDBD algorithm for the learning of appropri ate biases based on previous learning experience The IDBD algorithm is developed for the case of a simple linear learning system the LMS or delta rule with a separate learning rate parameter for each input The I...

Appropriate bias is widely viewed as the key to efficient learning and generalization. I present a new algorithm, the Incremental Delta-Bar-Delta (IDBD) algorithm, for the learning of appropriate biases based on previous learning experience. The IDBD algorithm is developed for the case of a simple, linear learning system-the LMS or delta rule with a separate learning-rate parameter for each inp...

In this paper we propose a method to implement in FPGA circuits, a feedforward neural network with on-chip delta rule learning algorithm. The method implies the building of a neural network by generic blocks designed in Mathworks’ Simulink environment. The main characteristics of this solution are on-chip learning algorithm implementation and high reconfiguration capability and operation under ...

This paper proposes an enhanced RBF network that enhances learning algorithms between input layer and middle layer and between middle layer and output layer individually for improving the efficiency of learning. The proposed network applies ART2 network as the learning structure between input layer and middle layer. And the autotuning method of learning rate and momentum is proposed and applied...

Abstract Various leaning method of neural networks including supervised and unsupervised methods are presented and illustrated with examples. General learning rule as a function of the incoming signals is discussed. Other learning rules such as Hebbian learning, perceptron learning, LMS Least Mean Square learning, delta learning, WTA – Winner Take All learning, and PCA Principal Component Analy...