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1

Photovoltaic power prediction based on improved grey wolf algorithm optimized back propagation

He Ping, Dong Jie, Wu Xiaopeng, Yun Lei, Yang Hua

Archives of Electrical Engineering

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2023

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Vol. 72, nr 3

613--628

EN

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

2

Non-Invasive Hemoglobin Monitoring Device Using K-Nearest Neighbor and Artificial Neural Network Back Propagation Algorithms

Munadi R., Sussi S., Fitriyanti N., Ramadan D. N.

International Journal of Electronics and Telecommunications

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2022

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Vol. 68, No. 1

13--18

EN

The invasive method of medically checking hemoglobin level in human body by taking the blood sample of the patient requiring a long time and injuring the patient is seen impractical. A non-invasive method of measuring hemoglobin levels, therefore, is made by applying the K-Nearest Neighbor (KNN) algorithm and the Artificial Neural Network Back Propagation (ANN-BP) algorithm with the Internet of Things-based HTTP protocol to achieve the high accuracy and the low end-to-end delay. Based on tests conducted on a Noninvasive Hemoglobin measuring device connected to Cloud Things Speak, the prediction process using algorithm by means of Python programming based on Android application could work well. The result of this study showed that the accuracy of the K-Nearest Neighbor algorithm was 94.01%; higher than that of the Artificial Neural Network Back Propagation algorithm by 92.45%. Meanwhile, the end-to-end delay was at 6.09 seconds when using the KNN algorithm and at 6.84 seconds when using Artificial Neural Network Back Propagation Algorithm.

3

Identifying region specifc seasonal crop for leaf borne diseases by utilizing deep learning techniques

Jayagopal Prabhu, Rajendran Sukumar, Mathivanan Sandeep Kumar, Sathish Kumar Sree Dharinya, Raja Kiruba Thangam, Paneerselvam Surekha

Acta Geophysica

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2022

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Vol. 70, no 6

2841--2854

EN

India economy depends on agriculture with severe climatic changes and a heavy infestation of diseases depleting food crop yield substantially. Rapid identification and real-time infestation feedback that affects plants are accomplished through computer vision and IoT, thereby providing a reliable system for farmers to increase the season’s growth yield. With LSTM, CNN provides an efficient way of identifying diseases specific leaf in plants through image recognition techniques. An extensive collection of plant leaf images is trained to recognize season-specific diseases like early blight and late blight, leaf mold, and yellow leaf curl. The proposed CNN model identifies the infestation with high accuracy and precision with significantly fewer training epochs. The proposed model provides an efficient way of identifying leaf borne infestation pertained to a particular agricultural region. Furthermore, there is a need to increase and improve different region-specific infestations that arise due to climatic and seasonal changes.

4

Deep back propagation–long short-term memory network based upper-limb sEMG signal classification for automated rehabilitation

Wang Ying, Wu Qun, Dey Nilanjan, Fong Simon, Ashour Amira S.

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 3

987--1001

EN

Autonomous rehabilitation training for assisted patients with injured upper-limbs promotes the regenerative communication between muscle signals and brain consciousness. Surface electromyographic (sEMG) is a type of electrical signals of neuromuscular activity recorded by electrodes on the surface of the human body, which is widely applied for detecting gestures and stimuli reactions. Experimental results proved the importance of the sEMG signals for extracting such reactions, in which, the segmentation and classification of the sEMG are vital tasks. The objective of the present work is to segment and classify the sEMG signals of patients to assist the design of clinical rehabilitation devices based on the classification of sEMG signals. In the pre-processing stage, a dual-tone multi-frequency signaling is designed for signal coding; subsequently, the pre-processed sEMG signal is transformed by the Fast Fourier Transfer. Afterward, a time-series frequency analysisis performed by applyingHiddenMarkov Models.A basic traditional longshort- term memory (LSTM) model is addressed for waveform-based classification to be compared to the proposed improved deep BP (back-propagation)–LSTM for sEMG signal classification. Seventeen performance features are selected for evaluating the proposed multi-classification, deep learning model for classifying six actions, namely moving gesture of grip, slowly moving, flexor, straight lift, stretch, and up-high lift; which were proposed by rehabilitation physician. The experiment results indicated the superiority of the proposed method compared to other well-known classifiers, such as the neural network, support vector machine, decision trees, Bayes inference, and recurrent neural network. The proposed deep BP–LSTM network achieved 92% accuracy, 89% specificity, 91% precision, and 96% F1-score, in the multi-classification of the sEMG signals.

5

Analysis of Multi Layer Neural Networks with Direct and Cross Forward Connection

Płaczek S., Adhikari B.

Fundamenta Informaticae

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2014

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Vol. 133, nr 2/3

227--240

EN

Artificial Neural Networks are of much interest for many practical reasons. As of today, they are widely implemented. Of many possible ANNs, the most widely used one is the backpropagation model with direct connection. In this model the input layer is fed with input data and each subsequent layers are fed with the output of preceding layer. This model can be extended by feeding the input data to each layer. This article argues that this new model, named Cross Forward Connection, is optimal than the widely used Direct Connection.

6

Optimization of Neural Network Model Design: An Electoral Cooperative Particle Swarm Optimization Approach

Li D., Xu H.

Przegląd Elektrotechniczny

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2013

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R. 89, nr 1b

165--167

EN

This paper proposes an electoral cooperative particle swarm optimization approach to optimize the model of neural network from both structure and linked weights. Different with other related research work, a new encoding method is adopted to divide the neural network into several modules, each of them corresponding to a sub-swarm. Based on the experiments on typical problems and classic dataset, the results show that the proposed algorithm outperforms all the compared ones in perspective of test error, correctness, connection number, and the CPU time of the training phase.

PL

W przedstawionym artykule opisano zastosowanie metod optymalizacji roju cząstek do optymalizacji struktury i współczynników wagowych sieci neuronowej. Zaimplementowano nową metodę analizy, do dzielenia podzielenia sieci na moduły, reprezentujące mniejsze roje. Weryfikacja eksperymentalna i porównanie z metodami klasycznymi wykazały wysoką sprawność i skuteczność analizy.

7

A Risk Assessment Model of Flood Based on Information Diffusion Method and BP Neural Network

Chen J., Jin Q., Wang H., Zhao S.

Przegląd Elektrotechniczny

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2012

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R. 88, nr 9b

33-36

EN

Climate change has caused more frequent floods in China which have already resulted in huge losses. Thus flood risk assessment and management is an important research topic. In this paper, a new model of flood risk assessment is proposed based on the information diffusion theory and the back propagation (BP) neural network. Due to the fact that flood statistics data are relatively short and often insufficient for flood risk assessment, the information diffusion method can transform imperfect flood historical data from a point in a traditional data sample to a fuzzy data set and obtain optimized data sample. Then, the optimized data are used to train neural networks with back propagation and can improve neural network adaptive ability. The flood data of Dongting Lake’s different encirclement dikes are used to assess the flood risk of Dongting Lake with the proposed model in this research. The results are consistent with the actual situation of Dongting Lake area, which thus verifies the model’s effectiveness for flood risk management. This method can be easily applied to effectively resolve problems of insufficient samples in flood risk assessment.

PL

W artykule zaprezentowano nowy model oceny ryzyka powodzi bazujący na teorii dyfuzji informacji I wykorzystujący sieci neuronowe. Dane statystyczne o powodziach są relatywnie krótkie i często niewystarczające do oceny ryzyka. W pierwszym etapie przetwarza się dane historyczne do otrzymania bardziej kompletnych danych. Te dane wykorzystane są do trenowania sieci neuronowych.

8

Ways of Selecting Internal Patterns in Multilayer Perceptron Network

Kolibabka M., Cader A., Siwocha A., Krupski M.

Journal of Applied Computer Science Methods

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2012

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Vol. 4 No. 1

63--73

EN

Creating and later learning one-way neural networks depends on many factors. Selecting many of them has estimated and experimental character. The suggested method is the Allows weakness of the influence of the not optimal choice of the net structure, also speed and momentum values are less influential in classic Back then Propagation Method. There are few modes of choosing elements to use in Followed algorithm.

9

FPGA Neural Network implementation for real time control

Benbouchama C., Sakhi S., Tadjine M., Bouridane A.

Archives of Control Sciences

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2007

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Vol. 17, no. 1

15-37

EN

This paper describes an efficient implementation of neural multi-layer networks on FPGA fabric (Field Programmable Gate Array). A back-propagation algorithm was used for the training task while implementation and synthesis tools are centered on the ISE 6.3 of Xilinx with the targeted components being VirtexII and VirtexIIPro. A fixed point and a floating point number representation were used for encoding real numbers and for data processing, respectively. The realization of the activation function was carried out according to three methods, for which the results of simulation and synthesis are also presented. The implementation performances were tested using an approximation of some linear and non-linear functions. Of particular importance, two experimental evaluations involving the speed and the position control of a DC motor are given to demonstrate the features of the adopted methodology.

10

A neural network used for the colour conversion from RGB to CMY

Horzyk A.

Zeszyty Naukowe Uniwersytetu Jagiellońskiego. Prace Informatyczne

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1999

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z. 9

59-73

EN

The use of the neural network in the solution of the RGB-to-CMY colour conversion problem is discussed in the paper. Ca\lassically, a colour conversion problem is solved in an approximate way. The architecture of neural networks, which gives the ability to get satisfactory results is presented. The method of learning the network based on the back propagation method with a controlled process of the change of learning parameters to shorten its time is constructed.

11

A hybrid neural network used for the colour conversion from RGB to CMY and a new method for choosing a learning set

Horzyk A.

Image Processing & Communications

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1998

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Vol. 4, no 1-2

37-46

EN

This paper presents the application of the neural network to the colour conversion problem from RGB to CMY [1]. Three different architecture of neural network are compared and the one which gives the best results is chosen, and the method of learning this network based on the back propagation method with a controlled process of change of learning parametres, particulary the number of patterns in a learning set, in order to shorten the time of learning, is presented.