IJAT Vol.4 No.2 pp. 178-183
doi: 10.20965/ijat.2010.p0178


Development of Neural-Net-Based Decision Support System for Mattress Patterns Using Particle Swarm Optimization

Mitsue Kato* and Toru Yamamoto**

*Teisei Gakuen Junior College, 1-26-13 Kohinata, Bunkyo-ku, Tokyo 112-8630, Japan

**Hiroshima University, 1-1-1 Kagamiyama, Higashi Hiroshima, Hiroshima 739-8511, Japan

December 1, 2009
January 8, 2010
March 5, 2010
neural network, evolutionary computation, learning, particle swarm optimization, metaheuristics

The particle swarm optimization (PSO) concept simulating a simplified social milieu, is optimization useful for solving nonconvex continuous optimization problems. We discuss a new learning algorithm for simultaneously adjusting connection weights in neural networks and user-specified parameters included in units. Based on our algorithm, neural network learning, e.g., learning cost or adaptability, can be improved, as demonstrated in mattress decision support system.

Cite this article as:
Mitsue Kato and Toru Yamamoto, “Development of Neural-Net-Based Decision Support System for Mattress Patterns Using Particle Swarm Optimization,” Int. J. Automation Technol., Vol.4, No.2, pp. 178-183, 2010.
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Last updated on Jan. 15, 2021