Elman network is a class of recurrent neural networks used for function approximation. The main problem of this class is that its structure has a set of global sigmoid functions at its hidden layer. That means that if the operating conditions of a process be identified, are changed the function approximation property of the network is degraded. This paper introduces a new version of the Elman n...

Abstract Aiming at the time-varying and fluctuating characteristics of photovoltaic power output, a GA-Elman model is proposed for short-term prediction active power. This can use historical value daily meteorological information to directly forecast power, which convenient dispatching department supply bureau obtain output data in advance enhance reliability supply. Firstly, input variables El...

in this paper, the crack detection and depth ratio estimation method are presented in beamlikestructures using elman networks. for this purpose, by using the frequencies of modes asinput, crack depth ratio of each element was detected as output. performance of the proposedmethod was evaluated by using three numerical scenarios of crack for fixed-simply supportedbeam consisting of a single crack...

A system based on a neural network framework is considered. We used two neural networks, an Elman network [1][2] and a Kohonen (concurrent) network [3], for a categorization task. The input of the system are objects derived from three general prototypes: circle, square, polygon. We varied the size and orientation of the objects in a continuous way. The system is trained using a new algorithm, b...

In this paper we examine Elman’s position (1999) on generalization in simple recurrent networks. Elman’s simulation is a response to Marcus et al.’s (1999) experiment with infants; specifically their ability to differentiate between novel sequences of syllables of the form ABA and ABB. Elman contends that SRNs can learn to generalize to novel stimuli, just as Marcus et al’s infants did. However...

Reducing the computational complexity is desired in speech coding algorithms. In this paper, three neural gain predictors are proposed which can function as backward gain adaptation module of low delay-code excited linear prediction (LD-CELP) G.728 encoder, recommended by International Telecommunication Union-Telecom sector (ITU-T, formerly CCITT). Elman, multilayer perceptron (MLP) and fuzzy A...

The gradient descent momentum and adaptive learning rate TD-DBP algorithm can improve the training speed and stability of Elman network effectively. BP algorithm is the typical supervised learning algorithm, so neural network cannot be trained on-line by it. For this reason, a new algorithm (TDDBP), which was composed of temporal difference (TD) method and dynamic BP algorithm (DBP), was propos...

In this paper, the gain in LD-CELP speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (PSO) algorithms to optimize the structure and parameters of neural networks. Elman, multi-layer perceptron (MLP) and fuzzy ARTMAP are the candidate neural models. The optimized number of nodes in the first and second hidden layers of El...