Name
Abstract | ||
|---|---|---|
This paper presents a new particle swarm optimization (PSO) algorithm for tuning parameters (weights) of neural networks. The new PSO algorithm is called fuzzy logic-based particle swarm optimization with cross-mutated operation (FPSOCM), where the fuzzy inference system is applied to determine the inertia weight of PSO and the control parameter of the proposed cross-mutated operation by using human knowledge. By introducing the fuzzy system, the value of the inertia weight becomes variable. The cross-mutated operation is effectively force the solution to escape the local optimum. Tuning parameters (weights) of neural networks is presented using the FPSOCM. Numerical example of neural network is given to illustrate that the performance of the FPSOCM is good for tuning the parameters (weights) of neural networks. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NSS.2009.39 | Gold Coast, QLD |
Keywords | Field | DocType |
tuning parameter,fuzzy system,neural network,proposed cross-mutated operation,new pso algorithm,fuzzy inference system,new particle swarm optimization,fuzzy logic-based particle swarm,inertia weight,cross-mutated operation,neural network optimization,probability density function,adaptive systems,forecasting,fuzzy logic,data mining,pso algorithm,human knowledge,neural nets,particle swarm optimization,fuzzy set theory,local optimum,artificial neural networks,fuzzy systems | Particle swarm optimization,Mathematical optimization,Computer science,Local optimum,Fuzzy logic,Stochastic neural network,Algorithm,Multi-swarm optimization,Fuzzy control system,Adaptive neuro fuzzy inference system,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-0-7695-3838-9 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. H. Ling | 1 | 609 | 40.29 |
| Hung T. Nguyen | 2 | 372 | 56.85 |
| K. Y. Chan | 3 | 0 | 0.34 |