Titlebook: Emerging Intelligent Computing Technology and Applications; 8th International Co De-Shuang Huang,Phalguni Gupta,Prashan Premaratne Conferen

羊欄

Venules

Interactive Evolutionary Algorithms with Decision-Maker’s Preferences for Solving Interval Multi-objtionary algorithm incorporating with a decision-maker (DM)’s preferences is presented to obtain their Pareto subsets which meet the DM’s preferences in this study. The proposed algorithm is applied to four MOPs with interval parameters and compared with other two algorithms. The experimental results

monochromatic

Precipitation Control for Mixed Solution Based on Fuzzy Adaptive Robust Algorithmivided into nominal model and lumped disturbance term which embodies modeling error, parameter uncertainties, disturbances and unmodeled dynamics. Fuzzy adaptive control was adopted to approach uncertain parameters of the system in real time; the impact of external disturbances was eliminated by rob

Disk199

B-Spline Neural Networks Based PID Controller for Hammerstein Systemsthe nonlinear static function in the Hammerstein system. The control signal is composed of a PID controller together with a correction term. In order to update the control signal, the multistep ahead predictions of the Hammerstein system based on the B-spline neural networks and the associated Jacob

DNR215

Constrained Multi-objective Particle Swarm Optimization Algorithmse of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiv

Curmudgeon

ALB

獨(dú)輪車(chē)

Regularized Complete Linear Discriminant Analysis for Small Sample Size Problemscriminant analysis (LDA) requires the within-class scatter matrix to be non-sigular, LDA cannot be directly applied to SSS problems. In this paper, regularized complete linear discriminant analysis (RCLDA) is proposed to solve SSS problems. RCLDA uses two regularized criterion to derive “regular” di

LAITY

Unsupervised Feature Selection for Multi-cluster Data via Smooth Distributed Scorethe feature number to measure the discriminating power of each feature, and the top ranked features are considered to represent the intrinsic multi-cluster structure of the original data. Among different algorithms, Multi-Cluster Feature Selection(MCFS) is one well designed algorithm for its superio

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