Fault Tolerance Simulation and Evaluation Tool for Artificial Neural Networks
نویسندگان
چکیده
This paper presents the FTSET tool for fault tolerance evaluation and improvement of Artificial Neural Networks. Fault tolerance is a characteristic of parallel distributed systems such as neural networks. Although there is a built-in fault tolerance in neural networks, it is possible to improve this characteristic, but changing the structure of an artificial neural network to improve its fault tolerance is a complex task because of the non-linear activation functions that can be used in the network. The software proposed in this work performs both the evaluation of fault tolerance capabilities and the improvement of this parameter. The solution implemented here for improving the fault tolerance was proposed by the authors and is partially discussed here. Copyright CONTROLO2008.
منابع مشابه
Detection of Single and Dual Incipient Process Faults Using an Improved Artificial Neural Network
Changes in the physicochemical conditions of process unit, even under control, may lead to what are generically referred to as faults. The cognition of causes is very important, because the system can be diagnosed and fault tolerated. In this article, we discuss and propose an artificial neural network that can detect the incipient and gradual faults either individually or mutually. The mai...
متن کاملA software Tool for Teaching Fault Tolerance in Artificial Neural Networks
This paper presents a software tool for teaching, evaluating and improving the fault tolerance of an Artificial Neural Network. Fault tolerance is a characteristic of parallel distributed systems such as neural networks. This property, though usually pointed out as one of the main advantages of the Neural Networks, is difficult to evaluate and can be a residual property allowing to maintain onl...
متن کاملA Matlab tool for analyzing and improving fault tolerance of artificial neural networks
FTSET is a software tool that deals with fault tolerance of Artificial Neural Networks. This tool is capable of evaluating the fault tolerance degree of a previously trained Artificial Neural Network given its inputs ranges, the weights and the architecture. The FTSET is also capable of improving the fault tolerance by applying a technique of splitting the connections of the network that are mo...
متن کاملAN INTELLIGENT FAULT DIAGNOSIS APPROACH FOR GEARS AND BEARINGS BASED ON WAVELET TRANSFORM AS A PREPROCESSOR AND ARTIFICIAL NEURAL NETWORKS
In this paper, a fault diagnosis system based on discrete wavelet transform (DWT) and artificial neural networks (ANNs) is designed to diagnose different types of fault in gears and bearings. DWT is an advanced signal-processing technique for fault detection and identification. Five features of wavelet transform RMS, crest factor, kurtosis, standard deviation and skewness of discrete wavelet co...
متن کاملFault Detection and Location in DC Microgrids by Recurrent Neural Networks and Decision Tree Classifier
Microgrids have played an important role in distribution networks during recent years. DC microgrids are very popular among researchers because of their benefits. Protection is one of the significant challenges in the way of microgrids progress. As a result, in this paper, a fault detection and location scheme for DC microgrids is proposed. Due to advances in Artificial Intelligence (AI) and s...
متن کامل