Titlebook: Advances in Swarm Intelligence; First International Ying Tan,Yuhui Shi,Kay Chen Tan Conference proceedings 2010 Springer-Verlag Berlin Hei

誘拐

斜坡

Application of the reference model,tween inputs and outputs based on non-linear functional composition. They have the advantages of self-learning, self-organizing, and self-adapting. It is practical to use ANN technology to carry out forecast of surface runoff.

羽毛長成

制造

LAPSE

Application of the reference model, problem of force identification. Both numerical simulations and experimental study are performed to demonstrate the effectiveness, robustness and applicability of the proposed method. It is potential that the proposed method is practical to the real-life application.

先鋒派

Chaos Optimization SVR Algorithm with Application in Prediction of Regional Logistics Demandlect parameters of SVR. The proposed approach is used for forecasting logistics demand of Shanghai, The experimental results show that the above method obtained lesser training relative error and testing relative error.

d-limonene

Cooperation Partners Selection for Multiple-Core-Type MPNpartners is to choose the multiple-core-type modular producing network cooperation partners in accordance with the principles of the lowest total cost, the shortest reaction time and the minimum risk and through two stages of the initial selection of core partner and the comprehensive evaluation of core partner combination.

auxiliary

支架

CRPSO-Based Integrate-and-Fire Neuron Model for Time Series Predictiontraining the IFN model in order to enhance its approximation and generalization capabilities. The proposed CRPSO-based IFN model is used for Mackey-Glass time series prediction problem. The experimental results demonstrate the superiority of CRPSO-based model in efficiency and robustness over the PSO algorithm, BP algorithms and GA.

一致性

FIR Cutoff Frequency Calculating for ECG Signal Noise Removing Using Artificial Neural Networkrt signals are lowpass frequency, a Finite Impulse Response (FIR) filter is adequate to remove the noise. In the next step, a dataset is configured for a multilayer perceptron (MLP) training with feedforward algorithm. Finally, the MLP is trained and results of cutoff frequency calculation are shown.