alexa Genetically Optimized Multiple ANFIS Based Discovery a
ISSN ONLINE(2319-8753)PRINT(2347-6710)

International Journal of Innovative Research in Science, Engineering and Technology
Open Access

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Research Article

Genetically Optimized Multiple ANFIS Based Discovery and Optimization of Catalytic Materials

Virendra Nayak1, Y.P. Banjare2, M. F. Qureshi3
  1. Ph.D. Scholar, Department of Mechanical Engineering, Dr. C.V. Raman University, Bilaspur, India
  2. Associate Professor, Department of Mechanical Engineering, Govt. Engineering College, Jagdalpur, India
  3. Professor, Department of Electrical Engineering, Govt. Polytechnic College, Narayanpur, India
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A soft computing technique based on the combination of Multiple Adaptive Neuro-Fuzzy Inference System (M-ANFIS) and a Genetic Algorithm (GA) has been developed for the discovery and optimization of new materials when exploring a high-dimensional space. This technique allows the experimental design in the search of new solid materials with high catalytic performance when exploring simultaneously a large number of variables such as elemental composition, manufacture procedure variables, etc. This integrated architecture (M-ANFIS+GA) allows one to strongly increase the convergence performance when compared with the performance of conventional GAs. It is described how both soft-computing techniques are built to work together. The proposed optimization architecture has been validated using two hypothetical functions, based on the modeled behavior of multi-component catalysts explored in the field of combinatorial catalysis. The method consists of following stages. First, prior to feature extraction, some preprocessing techniques, Secondly, the six salient feature sets are input into the multiple ANFIS combination with genetic algorithms (GAs) for discovery and optimization of new materials. The proposed method is applied for discovery and optimization of new materials and testing results show that the multiple ANFIS combination can reliably recognize, discover and optimize new materials, which has a better performance compared to the individual GA based on ANFIS.

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