Hybrid Improved Differential Evolution and Wavelet Neural Network with load forecasting problem of air conditioning

Liao, Gwo-Ching

doi:10.1016/j.ijepes.2014.04.014

Cited by 46 publications

(13 citation statements)

References 34 publications

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“…For single-layer feedforward neural networks with hidden nodes governed by additive sigmoid or RBF activation functions and based on ELM algorithm theories [20], an online sequential extreme learning machine (OSELM) is developed by [12] in a unified way to deal with industrial applications that the training data comes one by one or chunk by chunk. To explain the principles of the OSELM, suppose a SLFN of hidden nodes and RBF activation function can be expressed as below,…”

Section: Nn-oselmmentioning

confidence: 99%

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

Salih

Noor

Merhaban

et al. 2015

Advances in Artificial Intelligence

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A single hidden layer feedforward neural network (SLFN) with online sequential extreme learning machine (OSELM) algorithm has been introduced and applied in many regression problems successfully. However, using SLFN with OSELM as black-box for nonlinear system identification may lead to building models for the identified plant with inconsistency responses from control perspective. The reason can refer to the random initialization procedure of the SLFN hidden node parameters with OSELM algorithm. In this paper, a single hidden layer feedforward wavelet network (WN) is introduced with OSELM for nonlinear system identification aimed at getting better generalization performances by reducing the effect of a random initialization procedure.

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Section: Nn-oselmmentioning

confidence: 99%

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

Salih

Noor

Merhaban

et al. 2015

Advances in Artificial Intelligence

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“…That is why this algorithm is implemented in many applications and researches [8]. However, behind the advantages of Neural networks lies the slow training process [9].…”

Section: Introductionmentioning

confidence: 99%

Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection

Daru

Hanif

Widodo³

2021

Jurnal Informatika

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Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy.

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“…Over the past few years, some algorithms inspired by nature, such as particle swarm optimization (PSO) [38], the firefly algorithm (FA) [39], differential evolution (DE) [40], and the bat algorithm (BA) [41] have been successfully developed to solve single-objective problems. However, in multi-objective problems, the objective function of single-objective optimization algorithms cannot be balanced simultaneously, leading to unreasonable results.…”

Section: Introductionmentioning

confidence: 99%

Novel analysis–forecast system based on multi-objective optimization for air quality index

Wang

et al. 2019

Journal of Cleaner Production

105

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The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis-forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysisforecast system is proposed that consists of complexity analysis, data preprocessing, and optimize-forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multiobjective multi-verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring.

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Hybrid Improved Differential Evolution and Wavelet Neural Network with load forecasting problem of air conditioning

Cited by 46 publications

References 34 publications

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

Wavelet Network: Online Sequential Extreme Learning Machine for Nonlinear Dynamic Systems Identification

Improving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection

Novel analysis–forecast system based on multi-objective optimization for air quality index

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