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Combat multiple access interference(MAI) and near-far effect, to some degree, BP neural network can be better suppression interference, because of its use of the least squares method, prone to local minimum. In this paper, instead of using the MMSE algorithm for least squares and the addition of a new kind of constraints, and gain iterative formula of BP neural network blind multiuser detection algorithm,...
The algorithms of remote image approximate geometric correction are mainly based on the least squares method (LSM) about linear or nonlinear models. Their disadvantages lie in overfitting, poor generalizing ability, and enough amount samples demand, due to the principle of the empirical risk minimization (ERM). It is put forward that the geometric correction algorithm of remote image making's use...
Replacing Least Squares Method by Real coding based Accelerating Genetic Algorithm (RAGA), the parameters of time response function in the GM (1, 1) Model are optimized. Combined with BP Artificial Neural Networks Model, the Equa ldimension Gray Filling BP Neural Networks Model Based on RAGA is established. By this model, predicted the groundwater depth of Chuangye farm in Sanjiang Plain. The structural...
This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) and Principle Component Analysis(PCA), for classification of electroencephalogram (EEG) signals. Different mental tasks have been used to understand the process in our mind and we have chosen relaxation and imagination for our study. As well as normal conscious state, we have considered mental tasks performed in...
This paper introduces a new hybrid approach for training the adaptive network based fuzzy inference system (ANFIS). The previous works emphasized on gradient base method or least square (LS) based method. In this study we apply one of the swarm intelligent branches, named particle swarm optimization (PSO). The hybrid method composes PSO with gradient decent (GD) for training. We use PSO with some...
This brief deals with a state observation problem when the dynamic model of a plant contains an uncertainty or it is completely unknown (only smoothness properties are assumed to be in force). The dynamic neural network approach is applied in this informative situation. A new learning law, containing relay (signum) terms, is suggested to be in use. The nominal parameters of this procedure are adjusted...
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