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Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第6期28卷 6796-6810页

作者： Ebrahimi-Khusfi, Zohre Taghizadeh-Mehrjardi, Ruhollah Nafarzadegan, Ali Reza Univ Jiroft Fac Nat Resources Dept Nat Sci Jiroft Iran Univ Tubingen Dept Geosci Soil Sci & Geomorphol Tubingen Germany Ardakan Univ Fac Agr & Nat Resources Ardakan Iran Univ Hormozgan Dept Nat Resources Engn Bandar Abbas Hormozgan Iran

Accurate prediction of the dust concentration (DC) is necessary to reduce its undesirable environmental effects in different geographical areas. Although the adaptive neuro-fuzzy inference system (ANFIS) is a powerful model for predicting dust events, no attempt has been made to investigate its uncertainty and interpretability. In this study, therefore, the uncertainty of the ANFIS model was quantified using uncertainty estimation based on local errors and clustering methods. Furthermore, we used a model-agnostic interpretation to make the ANFIS model interpretable. In addition, we used the bat optimization algorithm (bat) to increase the prediction accuracy of the ANFIS model. Seven explanatory variables were chosen for predicting DC in the cold and warm months across semi-arid regions of Iran. The results showed that the ANFIS+bat model increased the correlation coefficient by 10% and 16% for predicting DC in the cold and warm months, respectively, compared with the ANFIS model. Furthermore, the uncertainty analysis indicated a lower prediction interval (i.e., lower uncertainty) for the ANFIS+bat model compared with the ANFIS model for predicting DC in the cold and warm months. In addition, the model-agnostic interpretation tool findings indicated the highest contributions of air temperature and maximum wind speed for predicting DC in the cold and warm months, respectively. Prediction of DC using the proposed model will allow decision-makers to better plan for measures to mitigate the risks of wind erosion and air pollution.

关键词： Air pollution ANFIS bat optimization algorithm Interpretability Machine learning Uncertainty

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Reliability improvement of future microgrid with mixed load models by optimal dispatch of DGs

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第4期29卷 e2816.1-e2816.21页

作者： Yammani, Chandrasekhar Prabhat, Pankaj Natl Inst Technol Warangal Dept Elect Engn Warangal Andhra Pradesh India

This paper presents a novel dispatch strategy of distributed generators (DGs) in microgrid for reliability improvement. Energy index of reliability is considered for improvement along with minimization of system losses, system voltage deviation, and cost of DGs. These objectives depend on many criteria like price of DG power, renewable DG power availability, and system loading. The dispatch strategy is developed using bat optimization algorithm. The proposed algorithm is implemented for four seasons: winter, spring, summer, and autumn. For each season, hourly optimal schedules are obtained while maintaining reliability and optimizing objectives. Further, the proposed algorithm is tested for future load enhancement and future price of renewable DGs. The effect of the future load and renewable DG price changes on optimal DG dispatch is tested with IEEE 33 bus distribution system having mixed loads like industrial, residential, and commercial. The feasibility and benefits of the proposed technique are demonstrated with obtained results.

关键词： bat optimization algorithm distributed generators (DGs) load models microgrid optimal DG scheduling reliability

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Meta-heuristic approaches for minimizing error in localization of wireless sensor networks

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APPLIED SOFT COMPUTING 2015年 36卷 506-518页

作者： Sivakumar, S. Venkatesan, R. PSG Coll Technol Dept Informat Technol Coimbatore Tamil Nadu India PSG Coll Technol Dept Comp Sci & Engn Coimbatore Tamil Nadu India

Sensor node localization is considered as one of the most significant issues in wireless sensor networks (WSNs) and is classified as an unconstrained optimization problem that falls under NP-hard class of problems. Localization is stated as determination of physical co-ordinates of the sensor nodes that constitutes a WSN. In applications of sensor networks such as routing and target tracking, the data gathered by sensor nodes becomes meaningless without localization information. This work aims at determining the location of the sensor nodes with high precision. Initially this work is performed by localizing the sensor nodes using a range-free localization method namely, Mobile Anchor Positioning (MAP) which gives an approximate solution. To further minimize the location error, certain meta-heuristic approaches have been applied over the result given by MAP. Accordingly, bat optimization algorithm with MAP (BOA-MAP), Modified Cuckoo Search with MAP (MCS-MAP) algorithm and Firefly optimization algorithm with MAP (FOA-MAP) have been proposed. Root mean square error (RMSE) is used as the evaluation metrics to compare the performance of the proposed approaches. The experimental results show that the proposed FOA-MAP approach minimizes the localization error and outperforms both MCS-MAP and BOA-MAP approaches. (C) 2015 Published by Elsevier B.V.

关键词： Meta-heuristics NP-hard problems Mobile Anchor Positioning bat optimization algorithm Modified Cuckoo Search Firefly optimization algorithm

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A novel formulation for predicting the shear strength of RC walls using meta-heuristic algorithms

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NEURAL COMPUTING & APPLICATIONS 2024年第15期36卷 8727-8756页

作者： Parsa, Payam Naderpour, Hosein Ezami, Nima Semnan Univ Fac Civil Engn Semnan Iran Toronto Metropolitan Univ Dept Civil Engn Toronto ON Canada Univ Toronto Dept Civil & Mineral Engn Toronto ON Canada GEI Consultants Inc Markham ON Canada

Reinforced concrete (RC) shear walls play a pivotal role in resisting seismic and lateral loads within structural frameworks. A thorough examination of the existing literature was undertaken, covering a range of experimental and theoretical studies related to the design of RC shear walls. It was emphasized that comprehending shear failure behavior and precisely predicting the shear strength of RC walls holds considerable significance. To address this, the study proposes two models that integrate the support vector regression method with meta-heuristic optimization algorithms (bat and GOA), utilizing 228 sets of experimental data. In identifying the parameters influencing the shear strength of RC shear walls, the study focused on eight influential factors. The comparison of the two proposed models in the current research with existing models and experimental data demonstrated their commendable accuracy, surpassing the performance of suggested empirical formulations. The prediction errors associated with the proposed models, when compared to experimental data, were notably low. An innovative approach was introduced in the research, presenting a novel method for predicting shear strength using the support vector regression method and the bat optimization algorithm. A notable advantage of this formulation lies in its capacity to predict the shear strength across various configurations, including squat, cylindrical, and thin RC shear walls. Unlike some existing equations for predicting shear strength, this formulation exhibits no limitations. Through a comparative analysis with established equations, the computational framework's results suggest its successful applicability in building codes and construction practices. The proposed method contributes to the accurate prediction of shear strength in diverse RC shear wall configurations, offering a valuable tool for structural engineering applications.

关键词： Reinforced concrete (RC) shear walls Shear strength Support vector regression bat optimization algorithm Grasshopper optimization algorithm SVR-bat SVR-GOA

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A Hybrid Three-Staged, Short-Term Wind-Power Prediction Method Based on SDAE-SVR Deep Learning and BA optimization

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IEEE ACCESS 2022年 10卷 123595-123604页

作者： Duan, Ruiqin Peng, Xiaosheng Li, Cong Yang, Zimin Jiang, Yan Li, Xiufeng Liu, Shuangquan Yunnan Power Grid Corp Ltd Syst Operat Dept Kunming 650011 Yunnan Peoples R China Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China

Wind power prediction (WPP) is necessary to the safe operation and economic dispatch of power systems. In order to improve the prediction accuracy of WPP, in this paper we propose a three-step model named SDAE-SVR-BA to be applied in short-term WPP based on stacked-denoising-autoencoder (SDAE) feature processing, bat algorithm (BA) optimization and support vector regression (SVR). First, we preprocessed the original NWP data input into the SDAE-SVR-BA model to adapt to the training and prediction of the proposed model. Second, we input the preprocessed features into the SDAE network, whose parameters are optimized by BA to obtain the depth-mapping features. Finally, we input the features of SDAE network mapping into SVR, whose parameters are optimized by BA for prediction, so as to obtain the SDAE-SVR-BA model. In this paper, we used BA during the training process to optimize the number of hidden layers and hidden layer nodes of SDAE, the penalty factor parameter C and the kernel function radius g of the SVR model. Additionally, we verified the model with a wind farm example and compared it to the traditional model. Based on the verification data applied in this article, in a forecast for the next twelve hours, the normalized root means square error (NRMSE) of SDAE-SVR was 11.97% and the NRMSE of SDAE-SVR-BA model was 11.54%, reduced by 1.24% compared with SDAE, which demonstrates the effectiveness of the proposed method.

关键词： optimization Predictive models Wind power generation Prediction algorithms Training Deep learning Data models Stack denoising autoencoder bat optimization algorithm wind power prediction

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Chaos-enhanced synchronized bat optimizer

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APPLIED MATHEMATICAL MODELLING 2020年第Part2期77卷 1201-1215页

作者： Yu, Helong Zhao, Nannan Wang, Pengjun Chen, Huiling Li, Chengye Jilin Agr Univ Coll Informat Technol Changchun 130118 Jilin Peoples R China Wenzhou Univ Coll Elect & Elect Engn Wenzhou 325035 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China

In this paper, a chaos-enhanced bat algorithm is proposed to tackle the global optimization problems. bat algorithm is a relatively new stochastic optimizer inspired by the echolocation behavior of bats in nature. Due to its effectiveness, it has been applied to many fields such as engineering design, feature selection, and machine learning. However, the classical approach is often prone to falling into local optima. This paper proposes an enhanced bat algorithm to alleviate this problem observed in the original algorithm. The proposed method controls the steps of chaotic mapping by a threshold and synchronizes the velocity of agents using a velocity inertia weight. These mechanisms are designed to boost the stability and convergence speed of the bat algorithm, instantly. Eighteen well-established and the state-of-the-art meta-heuristic approaches are considered to validate the effectiveness of the developed algorithm. Experimental results reveal that the proposed chaos-enhanced bat algorithm is not only superior to the well-established algorithms such as the original method but also the latest improved approaches. Also, the proposed method is successfully applied to I-beam design problems, welded beam design, and pressure vessel design. The results show that chaos-enhanced bat algorithm can deal with unconstrained and constrained feature spaces, effectively. (C) 2019 Elsevier Inc. All rights reserved.

关键词： bat optimization algorithm Function optimization Chaotic mapping I-beam design problems Welded beam design Pressure vessel design

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bat-inspired Metaheuristic Convolutional Neural Network algorithms for CAD-based Lung Cancer Prediction

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2021年第1期24卷 65-71页

作者： Priyadharshini, P. Zoraida, B. S. E. Bharathidasan Univ Dept Comp Sci & Engn Tiruchirappalli India

In this real-world, lung cancer (LC) is the foremost reason for mortality in both mankind in the present time, with an inspiring figure of around five million deaths every year. Computer tomography (CT) can deliver valuable information when diagnosing lung illnesses. The chief goal of this work is to identify cancer nodules in the lungs from a given input image of the lungs and to organize LC and its harshness. To locate cancer nodules in the lungs, Fuzzy c-means (FCM) based segmentation is used. In this paper, a bat optimization-based learning rate modified Convolutional Neural Network algorithm is introduced to effectively predict lung cancer. Additionally, to improve the proposed classification performance, input image is decomposed with support of the Discrete Wavelet Transform (DWT). With is used to decompose the image into four sub-bands, in such case we considered the Low (LL) band image. And then segmented images are split into two groups of images, which are used for the training and testing process. the proposed scheme has validated with the help of the LIDC-IDRI publically available dataset. They are studied by applying a convolutional neural network, and instantly trained neural network for predicting LC. In the end, the system efficiency is checked by using MATLAB tool to obtain the results of this model. In this experimentation, we achieved the accuracy of 97.43 % with a minimum classification error of 2.57 % in lung cancer prediction. This method is used to diagnose lung cancer correctly, and also this method may also overcome the previous drawbacks in the lung cancer diagnosis method.

关键词： Fuzzy c-means (FCM) Computer Tomography (CT) Convolutional Neural Network (CNN) bat optimization algorithm Discrete Wavelet Transform LC Prediction

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An intelligent fractional-order system for the biological parameters regulations

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2023年第4期11卷 1880-1894页

作者： Sahu, Tapaswini Tripathy, Madhab Chandra Sahoo, Satya Prakash BPUT Dept Elect & Telecommun Engn Rourkela 769015 Odisha India CET OUTR Dept Instrumentat & Elect Engn Bhubaneswar 751030 Odisha India VSSUT Dept Comp Sci & Engn Burla 768018 Odisha India

In recent times, controllers have been widely used for tuning the biological parameters of the human body. Biological parameters play a vital role in determining the health status of the human body. Any change/difference in biological components leads the person to severe diseases and even death. Therefore, an effective tuning mechanism is necessary to tune these biological parameters optimally. Several tuning models were proposed in the past based on control systems, but those models require more resources for tuning the biological parameters. Also, those models are highly complex in design and computational time. To find a solution for this described issue, a novel hybrid controller named bat-based recurrent fractional-order system has been developed in this article. The bat fitness is incorporated into the controller to provide the finest tuning outcomes. Here, the designed model tunes the biological parameters such as glucose, insulin, and gene expression. Besides, the classification layer of the recurrent neural system is tuned by the bat functions that have afforded better biological parameter prediction outcomes in a very short time. Furthermore, the developed controller is executed in a MATLAB environment, and the performance has been checked for biological data acquisition. In addition, the outcomes were evaluated based on settling time, overshoot, accuracy, and error rate;sensitivity and stability assessments were made to prove the system's performance. Furthermore, to carry the present research work further, the unstable range of the proposed model has been discussed along with the error rate. Subsequently, the executed model's outcomes were compared with the existing controller to verify the results. The implementation and comparative assessment proved that the designed controller had earned the finest tuned results. Hence, the developed model is more suitable for the biological instrumentation application for predicting and tuning the parameters to the

关键词： bat optimization algorithm Biological system Fractional-order controller Tuning mechanism

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Directional Radar-Embedded Communications Based on Hybrid MIMO and Frequency Diverse Arrays

Directional Radar-Embedded Communications Based on Hybrid MI...

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IEEE Radar Conference (RadarConf)

作者： Nusenu, Shaddrack Yaw Shao, Huaizong Wang, Wen-Qin Basit, Abdul Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Koforidua Tech Univ Koforidua Ghana Natl Nat Sci Fdn China NS FC Beijing Peoples R China Fdn Sichuan Prov Beijing Peoples R China

ISBN: (纸本)9781728116792

In this paper, we explore directional radar embedded communications where the embedded information symbols is transmitted to the desired receiver located in pre-specified direction(s) while simultaneously lowering the probability of interception by undesired receiver(s). We devise a two-dimensional (i.e., range and angle) directional modulation (DM) scheme for information symbols embedded into the radiation of frequency diverse multiple-input multiple-output (FD-MIMO) radar. In FD-MIMO array, orthogonal waveforms are adopted for radar functionality. We implement the communication operation by using bat optimization algorithm for DM which optimized the phase shifters continuously with time. Thus, each pair of symbols is designed and embedded in each orthogonal waveform. When the phase shifters are altered, the steering vector of the FD-MIMO radar is also changed according to the angle and range values specified. The hybrid FD-MIMO enjoys the advantages of MIMO in spatial diversity and the frequency diverse array in range-dependent coherent directional gain. Moreover, satisfactory hit error rate for communication operation is achieved.

关键词： Directional communication Frequency diverse array (FDA) MIMO bat optimization algorithm Radar embedded communication

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Speaker Verification with Optimized Feature Subset using MOBA

Speaker Verification with Optimized Feature Subset using MOB...

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IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Devi, J. Sirisha Nandyala, Siva Prasad Reddy, P. Vijaya Bhaskar JNTU H Dept CSE Hyderabad Andhra Pradesh India Tata Elxsi Bengaluru Karnataka India CMR Engn Coll Dept CSE Hyderabad Andhra Pradesh India

ISBN: (纸本)9781509016235

In speech processing for speaker verification, feature subset selection is one of the key components. Feature Subset Selection (FS) also played a vital role in the fields like pattern recognition, image processing, data mining, and gene selection. In a real world problem related to speech domain, speech sample contains a large number of relevant and irrelevant features. To increase the speaker verification rate, one needs to use the optimization technique for feature selection after the feature extraction technique. The ultimate goal is to select the most relevant subset of features for error free optimized classification in the speech domain. In this regard a novel feature subset selection algorithm is proposed using bat algorithm and Multi Objective optimization technique. Results of the experiment shows the proposed algorithm surpassed the accuracy rates shown by the conventional systems.

关键词： bat optimization algorithm feature subset selection multi objective optimization support vector machine K-nearest neighbor

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