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We discuss two types of SONFN architectures with the taxonomy based on the NFN scheme being applied to the premise part of SONFN and propose a comprehensive learning algorithm.
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We discuss two classes of SONFN architectures and propose comprehensive learning algorithms.
In this paper, a discrete comprehensive learning PSO algorithm, which uses acceptance criterion of simulated annealing algorithm, is proposed for Traveling Salesman Problem (TSP).
The algorithm's performance is studied with the comparison of real coded genetic algorithm (RGA), conventional PSO, comprehensive learning particle swarm optimization (CLPSO and Parkss and McClellan (PM) Algorithm.
Each design problem is optimized using genetic algorithm (GA) and four variants of PSO algorithms, namely global PSO (gbest), local PSO (lbest), comprehensive learning PSO (CLPSO), and modified local PSO (MLPSO).
The fifth is PSO algorithms with new efficient learning strategy: Comprehensive learning particle swarm optimizer (CLPSO) [49], Orthogonal particle swarm optimization (OPSO) [38], Orthogonal learning particle swarm optimization (OLPSO) [97], Genetic learning particle swarm optimization [35].
Learning Algorithm.
Then a machine learning algorithm stepped in.
While other previous researches used older machine learning algorithm in building their models, this research tries to implement some deep learning architectures to see the comparison by doing comprehensive analysis method through the accuracy result.
A machine- learning algorithm is subtly different from popular perception.
A fast learning algorithm for deep belief nets.
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