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PSO has been proved as an effective supervised learning system in recent years, but it's not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability...
With regards to the petrochemical processes with various operating states and dynamic performance which will affect estimation precision for the static soft sensor, a time series soft sensor model which uses the time series of process variables to estimate the dynamic performance of quality variable was proposed. Meanwhile, the integrated Adaboost learning algorithm is introduced. With the help of...
A learning algorithm for dynamic recurrent Elman neural networks is proposed based on an improved adaptive genetic algorithm. The proposed algorithm performs the evolution of network structure, weights, initial inputs of the context units and self-feedback coefficient of the modified Elman network together. Two dynamic identification algorithms for nonlinear systems are constructed successively based...
In this paper, algorithm of pattern extraction (Alopex) is introduced into the particle swarm optimization (PSO) to train the artificial neural network (ANN), which is used to construct the soft sensing model. PSO has some significant features such as simpler expression, less parameters and easier operation, but it is easily to run into the local optima. Alopex generates 'noises' randomly to get rid...
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