The stability of learning rate in neural network identifiers and controllers is one of the challenging issues which attracts great interest from researchers of neural networks. This paper suggests adaptive gradient descent algorithm with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of ...

Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control parameter optimization. We enhance this process by incorporating a neural network for tness evaluation. This neural network is trained to learn the complex interactions of the genetic algorithm control parameters and i...

In this paper we first show that standard BP algorithm cannot yeild to a uniform information distribution over the neural network architecture. A measure of sensitivity is defined to evaluate fault tolerance of neural network and then we show that the sensitivity of a link is closely related to the amount of information passes through it. Based on this assumption, we prove that the distribu...

Extracting the features from an image is a cumbersome task. Initially, this task was performed by domain experts through process known as handcrafted feature design. A deep embedding technique convolutional neural networks (CNNs) later solved problem introducing learning concept, which CNN directly provided with images. This then learns of image, are subsequently given input to further layers f...

in this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. utilizing the generalized characterization theorem. then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. here neural network is considered as a part of large eld called n...