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This paper presents a quantitative review of the influence and the impact of the two major soft computing approaches, Artificial Neural Network and Genetic Algorithm on cell formation methods of the design of Cellular Manufacturing System CMSS).
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Three major branches of soft computing, namely fuzzy logic, neural networks, and genetic algorithms, are discussed with respect to their applications in solving a variety of textile problems ranging from fiber classification, color grading, yarn and fabric property prediction, to the optimization of products and processes and even searching for a pleasing garment design.
The prediction of the contact forces was established by using a soft computing (computational intelligence) approach.
The major challenge with this approach is that the soft computing techniques, such as artificial intelligence and neural networks, are very difficult and complex to develop, analyse and apply in real-life scenarios.
Various architectures and different soft computing based approaches have been proposed to detect computer network attacks.
Although soft computing theory and techniques were first introduced in 1980s, it has now become a major research and study area in automatic control engineering.
His research interests include digital image and video processing, computer vision, medical imaging, machine learning, pattern analysis and recognition, neural networks, soft computing and bio-inspired computing techniques.
The two basic soft computing techniques are fuzzy computing and probabilistic computing.
This paper reviews the development of soft computing techniques.
A soft computing based prediction model was developed.
Intelligent soft computing scheme Support Vector Regression (SVR) is implemented.
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