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Metaheuristic optimization algorithms have become popular choice for solving complex and intricate problems which are difficult to solve by traditional methods. Particle swarm optimization has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its...
In our daily life, we need to get the accurate location information by GPS(Global Positioning System) receiver, GPS system is based on pseudo-range measurement, and there is four unknown parameter, but the navigation observation equation is nonlinear. The nonlinear equation can be solved by least squares iterative algorithm, the algorithm is based on linearization by Taylor model. The paper gives...
Particles can remember some information in an optimization process. They learn by themselves and from other particles, so the next generation can inherit much information from their parents and finally find optimal solutions. But particles are also faced with two problems of stagnating in a local but not global optimum. Genetic algorithms have strong global search ability. Genetic algorithms are combined...
The existing problems of Hopfield neural networks sovling travelling salesman problem are analysed and improved energy function is proposed in this paper. Probablity model is introduced into improved HNNs. Probablity model records the gene information of the best individuals,which can make genetic algorithm search simultaneously in depth and width. An improved genetic hopfield neural networks based...
In connection with the problem that disturbance of voltage and load change will lead to the performance deterioration of the conventional PID control in a DC speed regulation system, a two closed-loop fuzzy PID control method is presented. Through fuzzy logic reference, this method dynamically tunes the three parameters of PID controller adaptively according to the speed error e and its variety rate...
When GA is used to optimize neural networks, two problems need to be solved. One is the inbreeding and gene coding. Another is the balance between selection pressure and population diversity. One-to-one correspondence between the gene coding and functional equivalence class decreases the coding redundancy through normalizing coding of network. Adaptive crossover and mutation probability is proposed...
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