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Extra info for Real WorldApplns. of Genetic Algorithms [appl. math]
5) GHz. Given a FSS filter desired specification (f, BW), the goal of bio-inspired optimization is to find an optimal solution (εr, a) for a given fractal level k that minimizes the quadratic cost function as defined in (17). ( ) 2 ( ) f − fr pim BW − BW pim + E ( i , m) = f2 BW 2 2 (17) Application of Bio-Inspired Algorithms and Neural Networks for Optimal Design of Fractal Frequency Selective Surfaces 41 i i T The individuals pim = [ε rm , am ] evolve from the initial population given according to (18).
2004). Proceeding of the 18th Symposium of Malaysian Chemical Engineers. A. (2003). Dynamic Prediction of Milk Ultrafiltration Performance: A Neural Network Approach. Chem. Eng. Sci. , & Han, C. (1996). Intelligent systems in process engineering: a review. Comp. Chem. Eng. K. (2005). Multiobjective Optimization of An Industrial Styrene Monomer Manufacturing Process. Chem. Eng. Sci. , & Larachi, F. Integrated Genetic Algorithm – Artificial Neural Network Strategy for Modelling Important Multiphase-Flow Characteristics.
Iglesia, E. H. Eds. V. E. (1997). Introduction to the Design and Analysis of Experiments. London: Arnold Deb, K. (2001). , & Kogelschatz, U. (2000). Direct Conversion of Methane and Carbon Dioxide to Higher Hydrocarbons using Catalytic Dielectric-Barrier Discharges with Zeolites. Ind. Eng. Chem. Res. , & Manca, D. (2004). Modelling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes. Chem. Eng. Sci. G. Selective Hydrogenation of Acetylene to Ethylene during the Conversion of Methane in a Catalytic DC Plasma Reactor.