Studying the effect of training Levenberg Marquardt neural network by using hybrid meta-heuristic algorithms
Accelerated Particle Swarm Optimization (APSO) algorithm is one of the latest additions to the group of meta-heuristic nature inspired algorithms which provides derivative-free solutions to solve complex problems. Meanwhile, the Levenberg Marquardt Back propagation (LMBP) still it is not able to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
American Scientific Publishers
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/51019/ http://irep.iium.edu.my/51019/ http://irep.iium.edu.my/51019/ http://irep.iium.edu.my/51019/1/Attach_Lavenberg.pdf http://irep.iium.edu.my/51019/4/51019-Studying_the_effect_of_training_levenberg_marquardt_neural_network_by_using_hybrid_meta-heuristic_algorithms_SCOPUS.pdf |
| Summary: | Accelerated Particle Swarm Optimization (APSO) algorithm is one of the latest additions to the
group of meta-heuristic nature inspired algorithms which provides derivative-free solutions to solve
complex problems. Meanwhile, the Levenberg Marquardt Back propagation (LMBP) still it is not
able to avoid local minimum. To deal with this problem, global search optimization technique has
the ability to adjust the weight for NN (Neural Network) to avoid the local minima problem. This
paper proposes an accelerated particle swarm optimization (APSO) is implemented in conjunction
with Levenberg Marquardt back propagation (LMBP) algorithms to achieve faster convergence rate
and to avoid local minima problem. The performances of the proposed Accelerated Particle Swarm
Optimization Levenberg Marquardt (APSO_LM) algorithms compared by means of simulations on
7-Bit Parity and six UCI benchmark classification datasets. The simulation results show that the
APSO-LM algorithm shows better performance than baseline algorithms in terms of convergence
speed and Mean Squared Error (MSE). |
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