Optimization using chaotic neural network and its application to lighting design

• Autorzy: Nanba R. , Hasegawa M. , Nishita T. , Aihara K.
• Języki publikacji: EN

Abstrakty

EN

We have developed a chaotic neurodynamical searching method for solving the lighting design problems. The goal of this method is to design interior lighting that satisfies required illuminance distribution. We can obtain accurate illuminance distribution by using the radiosity method to calculate interreflection of lights. We formulate the lighting design problem that considers the interreflection of lights as a combinatorial optimization problem, and construct a chaotic neural network which searches the optimum solution of the lighting design problem. The calculated illuminance distribution is visualized using computer graphics. We compare this optimization method with the conventional neural network with gradient dynamics, simulated annealing, and the genetic algorithm, and clarify the effectiveness of the proposed method based on the chaotic neural network.

Słowa kluczowe

EN

chaotic neural networks; combinatorial optimization problem; computer graphics; genetic algorithm; lighting design; radiosity method; simulated annealing

PL

algorytm genetyczny; denaturacja symulowana; grafika komputerowa; metoda energetyczna; model świetlny; sieć neuronowa chaotyczna; zagadnienie optymalizacji kombinatorycznej

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2002
• Tom: Vol. 31, no 2
• Strony: 249--269
• Opis fizyczny: Bibliogr. 24 poz.,Rys., wykr.,

Twórcy

autor

Nanba R.

• Wireless Communications Division Communications Research Laboratory Independent Administrative Institution 3-4 Hikarino-oka, Yokosuka, Kanagawa 239-0847, Japan

autor

Hasegawa M.

• Wireless Communications Division Communications Research Laboratory Independent Administrative Institution 3-4 Hikarino-oka, Yokosuka, Kanagawa 239-0847, Japan

autor

Nishita T.

• Department of Complexity Science and Engineering Graduate School of Frontier Sciences The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

autor

Aihara K.

• CREST, JST 4-1-8 Hon-cho, Kawaguchi, Saitama 332-0012, Japan

Bibliografia

• AIHARA, K., NUMAGIRI, T., MATSUMOTO, G. and KOTANI, M. (1986) Structures of attractors in periodically forced neural oscillators. Phys. Lett. A, 116, 313- 317.
• AIHARA, K., TANABE , T. and TOYODA, M. (1990) Chaotic neural networks. Phys. Lett. A, 144, 6, 7, 333-340.
• CAIANIELLO, E. R. (1961) Outline of a theory of thought-processes and thinking machines. J. Theor. Biol., 2, 204- 235.
• CHEN, L. and AIHARA, K. (1995) Chaotic Simulated Annealing by a Neural Network Model with Transient Chaos. Neural Networks, 8, 915-930.
• COHEN, M.F. and GREENBERG, D.P. (1985) The Hemi-Cube: A Radiosity Solution for Complex Environments. Computer Graphics, 19, 3, 23 - 30.
• DoBASHI, Y., KANEDA, K., NAKASHIMA, T., YAMASHITA, H. and NrSHITA, T. (1995) A Quick Rendering Method using Basis Functions for Interactive Lighting Design. Computer Graphics Forum, 14, 3, C299-C240.
• DOBASHI, Y., NAKATANI, H., KANEDA, K. and YAMASHITA, H. (1998) An Interact ive Lighting Design System Int egrating Forward and Inverse Approach. The Institute of Image Electronics Engineers of Japan, 27, 4, 394- 359.
• GOLDBERG, D. (1989) Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.
• HASEGAWA, M., IKEGUCHI , T., MATOZAKI, T., and AIHARA, K. (1995) Solving Combinatorial Optimization Problems using Nonlinear Neural Dynamics Proc. of International Conference on Neural Networks, 6, 3140- 3145.
• HASEGAWA, M., lKEGUCHI. T. and AIHARA, K. (1997) Combination of Chaotic Neurodynamics with the 2-opt Algorithm to solve Traveling Salesman Problems. Physi cal Review Letters, 79, 12, 2344- 2347.
• HASEGAWA, M., lKEGUCHI, T. and AIHARA, K. (2000) Exponential and Chaotic Neurodynamical Tabu Searches for Quadratic Assignment Problems.
• HASEGAWA, M., IKEGUCHI, T and AIHARA, K. (2002) Solving Large Scale Traveling Salesman Problems by Chaotic Neurodynamics. Neural Networks, 15, 2, 271 - 283.
• HOPFIELD, J. J. and TANK, D. W. (1985) Neural Computation of Decisions in Optimization Prob lems. Biological Cybernetics, 52, 141-152.
• IMMEL, D.S., COHEN, M.F. and GREENBERG, D.P. (1985) A Radiosity Method for Non-Diffuse Environments. Computer Graphics, 20, 4, 23 - 30.
• KAWAI, J.K. and PAINT ER, J.S. and COHEN, M.F. (1993) Radioptimization: Goal Based Rendering. Computer Graphics Annual Conference Series, 147-154.
• KIRKPATRICK, S., GALATT, C. and VECCHI, M. (1983) Optimization by Simulated Annealing. Science, 220, 671 - 680.
• NAGUMO, J. and SATO, S. (1972) On a response characteristic of a mathematical neuron model. Kybernetik, 10, 155 - 164.
• NISHITA, T. and NAKAMAE, E. (1985) Continuous Tone Representation of Three-Dimensional Objects Taking Account of Shadows and Interreflection. Computer Gr-aphics, 19, 23 - 30.
• NOZAWA, H. (1992) A Neural Network Model as a Globally Coupled Map and Applications based on Chaos. Chaos, 2, 377- 386.
• SCHOENEMAN, C., DORSEY, J. J., SMITS, B., ARVO, J. and GREENBERG, D. (1993) Painting with Light. Computer Graphics Annua l Conference Series, 143- 146.
• TAKAHASHI, K., KANEDA, K., YAMANAKA, T., YAMASHITA, H., NAKAMAE, E. and NISHITA, T. (1993) Lighting Design in Interreflective Environments Using Hopficld Neural Networks. The iluminating Engineering Institute of Japan, 17, 2, 9- 15.
• WALLACE, J.R., COHEN, M.F. and GREENBERG, D.P. (1987) A Two-pass Solution to the Rendering Equation: A Synthesis of Ray Tracing and Radiosity Methods. Computer Graphics, 21 , 4, 311 - 320.
• WHITTED, T. (1980) An Improved Illumination Model for Shaded Display. Comm. ACM, 23, 6, 343- 349.
• YAMADA, T. and AIHARA, K. (1997) Nonlinear Neurodynamics and Combinatorial Optimization In Chaotic Neural Networks. Journal of Intelligent fj Fuzzy Systems, 5, 1, 53 - 68.