A novel fireworks factor and improved elite strategy based on back propagation neural networks for state-of-charge estimation of lithium-ion batteries.

The state of charge (SOC) of Lithium-ion battery is one of the key parameters of the battery management system. In the SOC estimation algorithm, the Back Propagation (BP) neural network algorithm is easy to converge to the local optimal solution, which leads to the problem of low accuracy based on the BP network. It is proposed that the Fireworks Elite Genetic Algorithm (FEG-BP) is used to optimize the BP neural network, which can not only solve the problem of the traditional neural network algorithm that is easy to fall into the local maximum optimal solution but also solve the limitation of the traditional neural network algorithm. The searchability of the improved algorithm has been significantly enhanced, and the error has become smaller and the propagation speed is faster. Combining the experimental data of charging and discharging, the proposed FEG-BP neural network is compared with the traditional genetic neural network algorithm (GA-BP), and the results are analyzed. The results show that the standard BP neural network genetic algorithm predicts error within 7%, while FEG-BP reduces the error to within 3%

The Application of Wavelet Neural Network in the Settlement Monitoring of Subway

The settlement monitoring of subway runs through the entire construction stage of subway. It is very important to predict the accurate settlement value for construction safety of subway. In this paper, the wavelet transform is used to denoise the settlement data. The auxiliary wavelet neural network, embedded wavelet neural network and single BP neural network are applied to predict the settlement of Tianjin subway. Compared with single BP neural network and auxiliary wavelet neural network, the embedded wavelet neural network model has a higher accuracy and better prediction effect. The embedded wavelet neural network is more valuable than the BP neural network model so it can be used in the prediction of subway settlemen

Study of Emotion Recognition Based on Electrocardiogram and RBF neural network

AbstractThis paper compares the emotional pattern recognition method between standard BP neural network classifier and RBF neural network classifier. The experiment introduces wavelet transform to analyze the Electrocardiogram (ECG) signal, and extracts maximum and standard deviation of the wavelet coefficients in every level. Then we construct the coefficients as eigenvectors and input them into BP and RBF neural network, then take a comparison of their experimental results. The result of experiment also show that the wavelet coefficients as the eigenvector can be effective characterization of ECG. The classification of the samples with BP neural network gets overall recognition rate of 87.5%, but RBF gets overall recognition rate of 91.67%. So compared with BP neural network, RBF has a better recognition rate for emotional pattern recognition

Recognition of Odor Characteristics Based on BP Neural Network

This paper introduces the basic principle and calculation steps of BP neural network algorithm for classification and prediction of odor characteristic parameters. Using the PEN3 electronic nose collects the volatile components of milk and programming BP neural network algorithm under MATLAB condition. This paper validate the use of BP neural network algorithm on milk quality prediction is effective

Stacking-Based Deep Neural Network: Deep Analytic Network for Pattern Classification

Stacking-based deep neural network (S-DNN) is aggregated with pluralities of basic learning modules, one after another, to synthesize a deep neural network (DNN) alternative for pattern classification. Contrary to the DNNs trained end to end by backpropagation (BP), each S-DNN layer, i.e., a self-learnable module, is to be trained decisively and independently without BP intervention. In this paper, a ridge regression-based S-DNN, dubbed deep analytic network (DAN), along with its kernelization (K-DAN), are devised for multilayer feature re-learning from the pre-extracted baseline features and the structured features. Our theoretical formulation demonstrates that DAN/K-DAN re-learn by perturbing the intra/inter-class variations, apart from diminishing the prediction errors. We scrutinize the DAN/K-DAN performance for pattern classification on datasets of varying domains - faces, handwritten digits, generic objects, to name a few. Unlike the typical BP-optimized DNNs to be trained from gigantic datasets by GPU, we disclose that DAN/K-DAN are trainable using only CPU even for small-scale training sets. Our experimental results disclose that DAN/K-DAN outperform the present S-DNNs and also the BP-trained DNNs, including multiplayer perceptron, deep belief network, etc., without data augmentation applied.Comment: 14 pages, 7 figures, 11 table

Comparison of Particle Swarm Optimization and Backpropagation as Training Algorithms for Neural Networks

Particle swarm optimization (PSO) motivated by the social behavior of organisms, is a step up to existing evolutionary algorithms for optimization of continuous nonlinear functions. Backpropagation (BP) is generally used for neural network training. Choosing a proper algorithm for training a neural network is very important. In this paper, a comparative study is made on the computational requirements of the PSO and BP as training algorithms for neural networks. Results are presented for a feedforward neural network learning a nonlinear function and these results show that the feedforward neural network weights converge faster with the PSO than with the BP algorithm