Soft computing techniques

Serra, J.M., Corma, A., Valero, S., Argente, E. and Botti, V. (2007) Soft computing techniques applied to combinatorial catalysis a new approach for the discovery and optimization of catalytic materials. QSAR Comb. Sci., 26, 11. 

Ciarapica, F.E. Giacchetta,G. (2009) Classification and prediction of occupational injury risk using soft computing techniques An Italian study, i t Scien 47 36-49. 

Predicting forces and damage in driiiing of poiymer composites soft computing techniques... 

The following sections highlight the application of soft computing techniques in the field of drilling of PMCs. 

The previous section dealt with application of soft computing techniques for developing predicting models for drilling forces and drilling-induced damage in PMCs. This section... 

The high degree of complexity of typical real problems implies that the final method used to solve a problem is more often a combination of several methods, such as soft computing techniques (namely, fuzzy logic, genetic algorithms, and neural networks) with more classic ones (such as algebraic, analytical, numerical, and stochastic methods). 

Using Soft Computing Techniques. An Integrated Neural Network-Fuzzy Logic-Genetic Algorithm Approach. 

An olefin epoxidation Ti-silicate-based catalyst was optimised by means of high-tbroughput experimentation for catalytic-materials synthesis, postsynthesis treatments, and catalytic testing. Soft-computing techniques for advanced experimental design and data assessment (GA, ANN) were used. The following variables were explored in the... 

Example 9.3-1 DoE framework for catalyst development based on soft computing techniques (Valero et al, 2009). 

Apart from ANN, new soft computing techniques like fuzzy logic, support vector machines and genetic algorithm etc. should also be explore for yam and fabric engineering. 

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