Improved Back Propagation Network Research Articles

Long-term wind speed prediction using artificial neural network-based approaches

<abstract> <p>In the current scenario, worldwide renewable energy systems receive renewed interest because of the global reduction of greenhouse gas emissions. This paper proposes a long-term wind speed prediction model based on various artificial neural network approaches such as Improved Back-Propagation Network (IBPN), Multilayer Perceptron Network (MLPN), Recursive Radial Basis Function Network (RRBFN), and Elman Network with five inputs such as wind direction, temperature, relative humidity, precipitation of water content and wind speed. The proposed ANN-based wind speed forecasting models help plan, integrate, and control power systems and wind farms. The simulation result confirms that the proposed Recursive Radial Basis Function Network (RRBFN) model improves the wind speed prediction accuracy and minimizes the error to a minimum compared to other proposed IBPN, MLPN, and Elman Network-based wind speed prediction models.</p> </abstract>

Application of Computed Tomography Imaging in Diagnosis of Endocrine Nerve of Gastric Cancer and Nursing Intervention Effect

In this article, some parameters and characteristics of computed tomography (CT) images in patients with gastric cancer are analyzed and the application of CT images in the diagnosis of gastric cancer endocrine nerves and the impact of nursing intervention on the quality and mental state of CT images of patients with gastric cancer are discussed. First, all patients were scanned with CT, and the CT values of the normal stomach wall and all lesions at different single-energy levels were recorded separately. Second, the improved back propagation network model was applied to realize the diagnosis of gastric cancer through the analysis of various features of CT images. The effect of nursing intervention on the image quality and mental state of CT imaging of patients with gastric cancer was studied. The results show that the energy spectrum curve of CT images and the improved back propagation network model are helpful for the initial diagnosis and identification of gastric cancer. Nursing intervention has a good influence on the clinical examination, image diagnosis, and psychological state of patients with gastric cancer, and it is easy for patients to undergo image diagnosis and examination according to correct operating procedures.

Novel back propagation optimization by Cuckoo Search algorithm.

The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way.

Emotion Speech Recognition under Sadness Conditions

Sadness, one of the negative emotions, may cause undesirable impact to the daily life. Therefore, it is desirable to automatically detect sadness emotion in human-machine interactions in order to adopt measures to impair the negative effects caused by it. Speech is one of the means used by human to express emotions, therefore, it is reasonable to detect sadness emotion using speech samples. In this paper, we analyzed relevant speech features, and proposed an improved Back Propagation (BP) network for sadness recognition. The experimental results show that the improved BP network proposed has better performance than traditional BP networks in detecting sadness emotion.

Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling

Based on the properties of neural network, an improved back-propagation network, including the structural organization, the training method and the generalization ability of network, was developed to simulate the relations between components of catalyst and aspects of catalytic performance, which include C 2 selectivity and conversion of methane. Levenberg–Marquardt method is presented to train the network and get better results than are available with traditional gradient method. The catalyst, which was found by SWIFT method, was proved to be better than any other catalyst in training pattern. When reacting on the optimum catalyst, GHSV was 33,313 cm 3 g −1 h −1, CH 4:O 2 was 3, reaction temperature was 1069 K, CH 4 conversion was 27.54%, C 2 selectivity reached 75.40% (C 2 yield was 20.77%) and the activity of catalyst did not decrease obviously in 10 h.

Neural network analysis of the EMG interference pattern

This paper investigates the performance of artificial neural networks for analysing and classifying EMG signals from healthy subjects and patients with myopathic and neuropathic disorders. EMG interference patterns (IP) were recorded under maximum voluntary contraction from the right biceps of a total of 50 subjects. Parameters were obtained from the signals using recognized quantification techniques including turns analysis, small segments analysis and frequency analysis. Supervised networks examined were an improved backpropagation network (IBPN), a radial basis network (RBN), and a learning vector quantization network (LVQ). Supervised networks using different combinations of parameters from turns analysis and small segments analysis gave diagnostic yields of 60–80%. Combinations using frequency analysis parameters produced similar results. The performance of unsupervised Self-Organising Feature Maps (SOFM) was generally lower than that of the supervised networks. Including personal data (sex and age) did not improve the overall performance.