Inverse Analysis of Scenery Evaluation of Planted Concrete Retaining Wall

Yasuda, Noboru; Chikata, Yasuo; Matsushima, Manabu; Kobori, Tameo

doi:10.2208/jscej.1995.514_55

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Similar to the results obtained using inverse analysis by GA, 14,15 in the present study, only some of the items contributed to the overall evaluation despite many items being available to choose from. The results obtained using neural network analysis (sensitivity analysis) are very different from the results of inverse analysis by GA for the following reasons.…”

Section: Inverse Analysis By Neural Networksupporting

confidence: 89%

“…The questionnaire was performed by 10 evaluators using 12 planted concrete retaining wall photographs. 2,5,13,14 In this article, to establish a simpler evaluation scheme, inverse analysis using a neural network that has excellent adaptability to problems containing nonlinear data was attempted. The same questionnaire results obtained in the abovementioned inverse analysis by GA were used in this study.…”

Section: Introductionmentioning

confidence: 99%

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Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Chikata

Yasuda

Matsushima

et al. 1998

Computer aided Civil Eng

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Scenery evaluation depends on the evaluator's experiences or perceptions. Thus inverse analysis by neural networks of scenery evaluation of planted concrete structures (concrete retaining walls) was examined in this study. In addition, in order to identify the evaluation schemes of evaluators, sensitivity analysis was performed on the obtained neural network structure. The efficacy of neural network inverse analysis and genetic algorithm analysis using fuzzy-set theory in reproduction of the same scenery evaluation questionnaire's results also was compared.

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Section: Inverse Analysis By Neural Networksupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Chikata

Yasuda

Matsushima

et al. 1998

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

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Improvement of particle swarm optimization based on the repetitive search guideline

Hiraoka¹,

Okamoto²,

Aiyoshi³

2010

Electrical Engineering Japan

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Particle Swarm Optimization (PSO), which has attracted a great deal of attention as a global optimization method in recent years, has the drawback that continuous search based on its excellent dynamic characteristics cannot be performed stably until the end of computation due to its very strong tendency to convergence. In this paper, we propose a "Repetitive Search Guideline" which differs from the common guidelines in the improved methods which have since been proposed and by which the continuous search in PSO is achieved without losing PSO's excellent dynamic characteristics due to repetitive search in a promising area where the objective function values are expected to be small. We consider four improved methods based on the proposed guidelines, then confirm their effectiveness by application to 100-variable multipeaked benchmark problems.

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Inverse Analysis of Scenery Evaluation of Planted Concrete Retaining Wall

Cited by 2 publications

References 5 publications

Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Inverse Analysis of Aesthetic Evaluation of Planted Concrete Structures by Neural Networks

Improvement of particle swarm optimization based on the repetitive search guideline

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