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Additional info for Ant Colony Optimization - Methods and Applications
7] extended the single hidden layer to two hidden layers for improve complex modeling problems. Pedrycz and Reformat designed fuzzy neural network constructed by AND, OR neurons to modeling the house price in Boston . We consider this multi-input-single-output (MISO) fuzzy logic-driven control system based on Pedrycz. Pedrycz transformed T norm and S norm into product and probability operators, formed a continuous and smooth function to be optimized by GA and BP. But there is no exactly symbolic expression for every node, because of the uncertain structure.
R. (1997). On the Use of Niching for Dynamic Landscapes, International Conference on Evolutionary Computation, IEEE. Cobb, H. G. & Grefenstette, J. J. (1993). Genetic Algorithms for Tracking Changing Environments, International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 523–530. ps Dorigo, M. & Gambardella, L. M. (1997). Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation 1(1): 53–66. Dorigo, M. & Gambardella, L.
Let us consider the homogeneous Markov process X t = ( Xb (t), τ (t)), where τ denotes the pheromone matrix. Since the real numbers are approximated on a computer by a subset of the set Q of the rational numbers, it is not restrictive in practice to assume that this process takes values in the set X × Q |A| . This last set can be partitoned as M X × Q| A| = k =1 Lk × Q| A| = M ( Lk × Q| A| ) = k =1 M k =1 Yk , where Yk = L k × Q | A| . Analogously as before, transitions between Y (t) ∈ Yj and Y (t + 1) ∈ Yk are allowed only if k > j.