Journal of Intelligent & Fuzzy Systems - Volume 39, issue 5

Authors: Elavarasan, Dhivya | Vincent, Durai Raj

Article Type: Research Article

Abstract: The development in science and technical intelligence has incited to represent an extensive amount ofdata from various fields of agriculture. Therefore an objective rises up for the examination of the available data and integrating with processes like crop enhancement, yield prediction, examination of plant infections etc. Machine learning has up surged with tremendous processing techniques to perceive new contingencies in the multi-disciplinary agrarian advancements. In this pa- per a novel hybrid regression algorithm, reinforced extreme gradient boosting is proposed which displays essentially improved execution over traditional machine learning algorithms like artificial neural networks, deep Q-Network, gradient boosting, ran- dom forest …and decision tree. Extreme gradient boosting constructs new models, which are essentially, decision trees learning from the mistakes of their predecessors by optimizing the gradient descent loss function. The proposed hybrid model performs reinforcement learning at every node during the node splitting process of the decision tree construction. This leads to effective utilizationofthesamplesbyselectingtheappropriatesplitattributeforenhancedperformance. Model’sperformanceisevaluated by means of Mean Square Error, Root Mean Square Error, Mean Absolute Error, and Coefficient of Determination. To assure a fair assessment of the results, the model assessment is performed on both training and test dataset. The regression diagnostic plots from residuals and the results obtained evidently delineates the fact that proposed hybrid approach performs better with reduced error measure and improved accuracy of 94.15% over the other machine learning algorithms. Also the performance of probability density function for the proposed model delineates that, it can preserve the actual distributional characteristics of the original crop yield data more approximately when compared to the other experimented machine learning models. Show more

Keywords: Crop yield prediction, reinforcement learning, extreme gradient boosting, intelligent agrarian application

DOI: 10.3233/JIFS-200862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7605-7620, 2020

Authors: Zhao, Tao | Li, Haodong | Dian, Songyi

Article Type: Research Article

Abstract: In this paper, we propose a method to assess the collision risk and a strategy to avoid the collision for solving the problem of dynamic real-time collision avoidance between robots when a multi-robot system is applied to perform a given task collaboratively and cooperatively. The collision risk assessment method is based on the moving direction and position of robots, and the collision avoidance strategy is based on the artificial potential field (APF) and the fuzzy inference system (FIS). The traditional artificial potential field (TAPF) has the problem of the local minimum, which will be optimized by improving the repulsive field …function. To adjust the speed of the robot adaptively and improve the security performance of the system, the FIS is used to plan the speed of robots. The hybridization of the improved artificial potential field (IAPF) and the FIS will make each robot safely and quickly find a collision-free path from the starting position to the target position in a completely unknown environment. The simulation results show that the strategy is effective and useful for collision avoidance in multi-robot systems. Show more

Keywords: Multi-robot, collision avoidance, path planning, improved artificial potential field, fuzzy inference system

DOI: 10.3233/JIFS-200869

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7621-7637, 2020

Authors: Wang, Hongyan | Huang, Zhi | Lu, Jinbo

Article Type: Research Article

Abstract: In this paper, by replacing the integral mass flow equation to fractional-order mass flow equation, the fractional-order mathematical model of 2DOF pneumatic-hydraulic upper limb rehabilitation training system is established. A new 2DOF fractional-order fuzzy PID (FOFPID) controller is designed, to provides a new reference for improving the control accuracy of the pneumatic system. In the design of the controller, the weight parameters of the input terms are transformed into the weight parameters of the error, and the input, which are analyzed to improve the accuracy of the controller design. The parameters of the control system are determined by multi-objective particle …swarm optimization. To prove the effectiveness of the proposed control method, the experimental research was carried out by building the experimental platform of pneumatic-hydraulic upper limb rehabilitation training system. The results show that the 2DOF FOFPID controller has better performance than other designed controllers under different working conditions. Show more

Keywords: Pneumatic-hydraulic drive, rehabilitation training system, fractional-order modeling, fractional-order fuzzy PID control

DOI: 10.3233/JIFS-200891

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7639-7651, 2020

Authors: Kumar, Ranjan | Edalatpanah, SA | Mohapatra, Hitesh

Article Type: Research Article

Abstract: There are different conditions where SPP play a vital role. However, there are various conditions, where we have to face with uncertain parameters such as variation of cost, time and so on. So to remove this uncertainty, Yang et al. [1 ] “[Journal of Intelligent & Fuzzy Systems, 32(1), 197-205”] have proposed the fuzzy reliable shortest path problem under mixed fuzzy environment and claimed that it is better to use their proposed method as compared to the existing method i.e., “[Hassanzadeh et al.; A genetic algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths, Mathematical and Computer Modeling, …57(2013) 84-99”  [2 ]]. The aim of this note is, to highlight the shortcoming that is carried out in Yang et al. [1 ] article. They have used some mathematical incorrect assumptions under the mixed fuzzy domain, which is not true in a fuzzy environment. Show more

Keywords: normal fuzzy number, Shortest path problem (SPP), fuzzy shortest path problem (FSPP), mixed fuzzy environment

DOI: 10.3233/JIFS-200923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7653-7656, 2020

Authors: Zhou, Linyong | You, Shanping | Ren, Bimo | Yu, Xuhong | Xie, Xiaoyao

Article Type: Research Article

Abstract: Pulsars are highly magnetized, rotating neutron stars with small volume and high density. The discovery of pulsars is of great significance in the fields of physics and astronomy. With the development of artificial intelligent, image recognition models based on deep learning are increasingly utilized for pulsar candidate identification. However, pulsar candidate datasets are characterized by unbalance and lack of positive samples, which has contributed the traditional methods to fall into poor performance and model bias. To this end, a general image recognition model based on adversarial training is proposed. A generator, a classifier, and two discriminators are included in the …model. Theoretical analysis demonstrates that the model has a unique optimal solution, and the classifier happens to be the inference network of the generator. Therefore, the samples produced by the generator significantly augment the diversity of training data. When the model reaches equilibrium, it can not only predict labels for unseen data, but also generate controllable samples. In experiments, we split part of data from MNIST for training. The results reveal that the model not only behaves better classification performance than CNN, but also has better controllability than CGAN and ACGAN. Then, the model is applied to pulsar candidate dataset HTRU and FAST. The results exhibit that, compared with CNN model, the F-score has increased by 1.99% and 3.67%, and the Recall has also increased by 6.28% and 8.59% respectively. Show more

Keywords: Generative adversarial nets, convolutional neural network, unbalanced dataset, pulsar candidate identification

DOI: 10.3233/JIFS-200925

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7657-7669, 2020

Authors: Liu, Xuning | Zhang, Guoying | Zhang, Zixian

Article Type: Research Article

Abstract: The feature selection of influencing factors of coal and gas outbursts is of great significance for presenting the most discriminative features and improving prediction performance of a classifier, the paper presents an effective hybrid feature selection and modified outbursts classifier framework which aims at solving exiting coal and gas outbursts prediction problems. First, a measurement standard based on maximum information coefficient(MIC) is employed to identify the wide correlations between two variables; Second, based on a ranking procedure using non-dominated sorting genetic algorithm(NSGAII), maximum relevance minimum redundancy(MRMR) algorithm is subsequently performed to find out candidate feature set highly related to the …class label and uncorrelated with each other; Third, random forest(RF) is employed to search the optimal feature subset from the candidate feature set, then the optimal feature subset that influences the classification performance of coal and gas outbursts is obtained; Finally, an improved classifier model has been proposed that combines gradient boosting decision tree(GBDT) and k-nearest neighbor(KNN) for outbursts prediction. In the modified classifier model, the GBDT is utilized to assign different weights to features, then the weighted features are input into the KNN to verify the effectiveness of proposed method on coal and gas outbursts dataset. The experimental results conclude that our proposed scheme is effective in the number of feature and prediction accuracy when compared with other related state-of-the-art prediction models based on feature selection for coal and gas outbursts. Show more

Keywords: Coal and gas outbursts, Maximum information coefficient, Non-dominated sorting genetic algorithm, Maximum relevance minimum redundancy, Random forest, Gradient boosting decision tree, K-nearest neighbor

DOI: 10.3233/JIFS-200937

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7671-7691, 2020

Authors: Guo, Jingni | Xu, Junxiang | Liao, Wei

Article Type: Research Article

Abstract: The multimodal transport network in the region with complex environment and being easily affected by disturbance factors is used as the research object in our work. The characteristics of the cascading failure of such multimodal transport network were analyzed. From the perspective of network load redistribution, the risk control methods for the cascading failure of the multimodal transport network were investigated. This research aims to solve the problem that traditional load redistribution methods usually ignore the original-destination (OD) constraint and uncertain risks. The conditional value-at-risk (CVaR) was improved based on the Bureau of Public Roads (BPR) road impedance function to …quantify the uncertainty of the disturbance factors. A nonlinear programming model was established with the generalized travel time as the objective function. A parallelly-running cellular ant colony algorithm was designed to solve the model. Empirical analysis was conducted on the multimodal transport network in Sichuan-Tibet region of China. The results of the empirical analysis verified the applicability of the proposed load redistribution method to such kind of regions and the effectiveness of the algorithm. This research provides theoretical basis and practical reference for the risk control of the cascading failure of multimodal transport networks in some regions. Show more

Keywords: Uncertain disturbance, multimodal transport network, risk control, load redistribution, cellular ant colony algorithm

DOI: 10.3233/JIFS-200968

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7693-7704, 2020

Authors: Kachouei, Mohammad | Ebrahimnejad, Ali | Bagherzadeh-Valami, Hadi

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The …proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results. Show more

Keywords: Data envelopment analysis, undesirable outputs, fuzzy numbers, common set of weights

DOI: 10.3233/JIFS-201022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7705-7722, 2020

Authors: Ali, Mohamed R. | Hadhoud, Adel R. | Ma, Wen-Xiu

Article Type: Research Article

Abstract:  In this approximation study, a nonlinear singular periodic model in nuclear physics is solved by using the Hermite wavelets (HW) technique coupled with a numerical iteration technique such as the Newton Raphson (NR) one for solving the resulting nonlinear system. The stimulation of offering this numerical work comes from the aim of introducing a consistent framework that has as effective structures as Hermite wavelets. Two numerical examples of the singular periodic model in nuclear physics have been investigated to observe the robustness, proficiency, and stability of the designed scheme. The proposed outcomes of the HW technique are compared with available …numerical solutions that established fitness of the designed procedure through performance evaluated on a multiple execution. Show more

Keywords: Singular periodic systems in nuclear physics, Hermite wavelets, hybrid approach, Gaussian formula of integration, collocation technique

DOI: 10.3233/JIFS-201045

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7723-7731, 2020

Authors: Cao, Jing | Xu, Xuan-hua | Dai, Fei | Pan, Bin

Article Type: Research Article

Abstract: This study uses opinion dynamics to explore the influence of extremists in the consensus process of large group decision-making. When moderates are exposed to extremists, their risk preference will be affected. By using the opinion leader theory for reference, the influence model of extremists is constructed. To better study the influence of extremists, the similarity of risk preference between extremists and moderates is modeled to measure their similarity degree. From this model, for every moderate, the extremists are divided into two groups: homogeneous group and heterogeneous group. Finally, the risk preference evolution model is structured by considering that moderates change …their risk preference dynamically according to their initial preference, their attitude towards the homogeneous groups, and the heterogeneous groups. Finding from data analysis shows that moderates with high acceptance toward the influence of extremists are more likely to reach group consensus. It is also found that the preference trend of moderates with a certain degree of acceptance toward heterogeneous groups fluctuates with a ‘W’ shape. This study bridges the gap between opinion dynamics and group decision making. Meanwhile, the model inspires new explanations and new perspectives for the group consensus process. Show more

Keywords: Extremists, opinion dynamics, group emergency decision-making, group consensus, risk preference evolution

DOI: 10.3233/JIFS-201106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 5, pp. 7733-7746, 2020