On the Introduction of Neural Network-based Optimization Algorithm in an Automated Calibration System

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

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Research on the optimization algorithm of logistic unload container based on Hopfield neural network

China-Ireland International Conference on Information and Communications Technologies (CIICT 2007) ◽

10.1049/cp:20070793 ◽

2007 ◽

Author(s):

Xiaoyun Wang ◽

Zhongming Zhang ◽

Li Yang

Keyword(s):

Neural Network ◽

Optimization Algorithm ◽

Hopfield Neural Network

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An image denoising method based on BP neural network optimized by improved whale optimization algorithm

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-02013-2 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Chunzhi Wang ◽

Min Li ◽

Ruoxi Wang ◽

Han Yu ◽

Shuping Wang

Keyword(s):

Neural Network ◽

Image Denoising ◽

Optimization Algorithm ◽

Bp Neural Network ◽

Weight Coefficient ◽

Whale Optimization Algorithm ◽

Denoising Method ◽

Training Time ◽

Whale Optimization ◽

City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

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Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0213 ◽

2020 ◽

Vol 65 (6) ◽

pp. 759-773

Author(s):

Segu Praveena ◽

Sohan Pal Singh

Keyword(s):

Neural Network ◽

Acute Lymphoblastic Leukaemia ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

Lymphoblastic Leukaemia ◽

Input Image ◽

Deep Convolutional Neural Network ◽

Grey Wolf ◽

Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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