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Fundamentals of neural networks
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The article was published on 1993-01-01 and is currently open access. It has received 1921 citations till now. The article focuses on the topics: Time delay neural network & Physical neural network.read more
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A Neural-Network Approach for Defect Recognition in TFT-LCD Photolithography Process
TL;DR: In this article, a neural network approach was proposed for defect recognition in the TFT-LCD photolithography process, and four neural network methods were adopted for this purpose, namely, backpropagation, radial basis function, learning vector quantization, and quantization quantization 2.
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Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite, short carbon fibers (CF) and nano-TiO2 using adaptive neuro-fuzzy inference system (ANFIS)
TL;DR: The obtained results showed that ANFIS is a powerful tool in modeling specific wear rate, and present integrate performance of neural network (NN) and fuzzy system (FS) and it is suggested a modeling method to predict and analyze the effectiveness of parameters of Specific wear rate.
Journal Article
Optimizing Weights of Artificial Neural Networks using Genetic Algorithms
Kinjal Jadav,Mahesh Panchal +1 more
TL;DR: Genetic algorithms are a class of optimization procedures which are well suited to the problem of training and optimize weights of Artificial Neural Networks and are shown in this paper.
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An expert system for perfume selection using artificial neural network
TL;DR: The objective of this research is to help customers in purchasing perfumes, with the aid of the expert system program developed by using artificial neural networks, and the model demonstrates the usefulness of 70.33% classification rate in classifying consumers' styles that looks satisfying.
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Time-Domain Features And Probabilistic Neural Network For The Detection Of Vocal Fold Pathology
TL;DR: The experimental results show that the proposed time-domain features gives very promising classification accuracy and can be effectively used to detect the vocal fold pathology clinically.