This paper can reasonably identify the connection weight and threshold of NN and improve capability of true problem solution. This paper also applies PSO-BP NN model into classification of fabric flaws, decomposes the fabric image at single layer and extract the sub-image in horizontal and vertical direction to represent longitudinal and latitudinal texture by using orthogonal wavelet transform...

To comprehensively understand the characteristics of gas nanosensor between temperature and sensitivity, this paper has developed a Backward Propagation (BP) neural network based on Particle Swarm Optimization (PSO), which is applied to fitting the temperature-sensitivity characteristic of the SnO2 gas nanosensor mixed with benzene. The simulation results show the PSO can well optimize the stru...

Given the relative limitations of BP and GA based leaning algorithms, Particle Swarm Optimization (PSO) is proposed to train Artificial Neural Networks (ANN) for the diagnosis of unexplained syncope. Compared with BP and GA based training techniques, PSO based learning method improves the diagnosis accuracy and speeds up the convergence process. Experimental results show that PSO is a robust tr...

The particle swarm optimization algorithm was showed to converge rapidly during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, the gradient descending method can achieve faster convergent speed around global optimum, and at the same time, the convergent accuracy can be higher. So in this paper, a hybrid algorithm comb...

With the improvement in integration of solar power generation, photovoltaic (PV) forecasting plays a significant role ensuring operation security and stability grids. At present, widely used backpropagation (BP) improved BP neural network algorithm short-term output prediction PV stations own drawbacks neglection meteorological factors weather conditions inputs. Meanwhile, existing traditional ...

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural netwo...