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Sledge IJ, Principe JC. An Exact Reformulation of Feature-Vector-Based Radial-Basis-Function Networks for Graph-Based Observations. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2020; 31:4990-4998. [PMID: 31902772 DOI: 10.1109/tnnls.2019.2953919] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Radial basis function (RBF) networks are traditionally defined for sets of vector-based observations. In this brief, we reformulate such networks so that they can be applied to adjacency-matrix representations of weighted, directed graphs that represent the relationships between object pairs. We restate the sum-of-squares objective function so that it is purely dependent on entries from the adjacency matrix. From this objective function, we derive a gradient descent update for the network weights. We also derive a gradient update that simulates the repositioning of the radial basis prototypes and changes in the radial basis prototype parameters. An important property of our radial basis function networks is that they are guaranteed to yield the same responses as conventional radial basis networks trained on a corresponding vector realization of the relationships encoded by the adjacency matrix. Such a vector realization only needs to provably exist for this property to hold, which occurs whenever the relationships correspond to distances from some arbitrary metric applied to a latent set of vectors. We, therefore, completely avoid needing to actually construct vectorial realizations via multidimensional scaling, which ensures that the underlying relationships are totally preserved.
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Chen J, Li Q, Wang H, Deng M. A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 17:E49. [PMID: 31861677 PMCID: PMC6982166 DOI: 10.3390/ijerph17010049] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 12/07/2019] [Accepted: 12/17/2019] [Indexed: 11/16/2022]
Abstract
The Yangtze River Delta (YRD) is one of the most developed regions in China. This is also a flood-prone area where flood disasters are frequently experienced; the situations between the people-land nexus and the people-water nexus are very complicated. Therefore, the accurate assessment of flood risk is of great significance to regional development. The paper took the YRD urban agglomeration as the research case. The driving force, pressure, state, impact and response (DPSIR) conceptual framework was established to analyze the indexes of flood disasters. The random forest (RF) algorithm was used to screen important indexes of floods risk, and a risk assessment model based on the radial basis function (RBF) neural network was constructed to evaluate the flood risk level in this region from 2009 to 2018. The risk map showed the I-V level of flood risk in the YRD urban agglomeration from 2016 to 2018 by using the geographic information system (GIS). Further analysis indicated that the indexes such as flood season rainfall, urban impervious area ratio, gross domestic product (GDP) per square kilometer of land, water area ratio, population density and emergency rescue capacity of public administration departments have important influence on flood risk. The flood risk has been increasing in the YRD urban agglomeration during the past ten years under the urbanization background, and economic development status showed a significant positive correlation with flood risks. In addition, there were serious differences in the rising rate of flood risks and the status quo among provinces. There are still a few cities that have stabilized at a better flood-risk level through urban flood control measures from 2016 to 2018. These results were basically in line with the actual situation, which validated the effectiveness of the model. Finally, countermeasures and suggestions for reducing the urban flood risk in the YRD region were proposed, in order to provide decision support for flood control, disaster reduction and emergency management in the YRD region.
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Affiliation(s)
- Junfei Chen
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
| | - Qian Li
- Business School, Hohai University, Nanjing 211100, China;
| | - Huimin Wang
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
| | - Menghua Deng
- State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China; (H.W.); (M.D.)
- Business School, Hohai University, Nanjing 211100, China;
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Chai Z, Song W, Bao Q, Ding F, Liu F. Taking advantage of hybrid bioinspired intelligent algorithm with decoupled extended Kalman filter for optimizing growing and pruning radial basis function network. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180529. [PMID: 30839667 PMCID: PMC6170552 DOI: 10.1098/rsos.180529] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Accepted: 08/15/2018] [Indexed: 06/09/2023]
Abstract
The growing and pruning radial basis function (GAP-RBF) network is a promising sequential learning algorithm for prediction analysis, but the parameter selection of such a network is usually a non-convex problem and makes it difficult to handle. In this paper, a hybrid bioinspired intelligent algorithm is proposed to optimize GAP-RBF. Specifically, the excellent local convergence of particle swarm optimization (PSO) and the extensive search ability of genetic algorithm (GA) are both considered to optimize the weights and bias term of GAP-RBF. Meanwhile, a competitive mechanism is proposed to make the hybrid algorithm choose the appropriate individuals for effective search and further improve its optimization ability. Moreover, a decoupled extended Kalman filter (DEKF) method is introduced in this study to reduce the size of error covariance matrix and decrease the computational complexity for performing real-time predictions. In the experiments, three classic forecasting issues including abalone age, Boston house price and auto MPG are adopted for extensive test, and the experimental results show that our method performs better than PSO and GA these two single bioinspired optimization algorithms. What is more, our method via DEKF achieves the better results in comparison with the state-of-art sequential learning algorithms, such as GAP-RBF, minimal resource allocation network, resource allocation network using an extended Kalman filter and resource allocation network.
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Affiliation(s)
- Zhilei Chai
- School of Internet of Things (IOT) Engineering, Jiangnan University, Wuxi, Jiangsu, China
- Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, Wuxi, Jiangsu, China
| | - Wei Song
- School of Internet of Things (IOT) Engineering, Jiangnan University, Wuxi, Jiangsu, China
- Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, Jiangsu, China
- Engineering Research Center of Internet of Things Applied Technology, Ministry of Education, Wuxi, Jiangsu, China
| | - Qinxin Bao
- School of Internet of Things (IOT) Engineering, Jiangnan University, Wuxi, Jiangsu, China
| | - Feng Ding
- School of Internet of Things (IOT) Engineering, Jiangnan University, Wuxi, Jiangsu, China
| | - Fei Liu
- School of Internet of Things (IOT) Engineering, Jiangnan University, Wuxi, Jiangsu, China
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Abpeykar S, Ghatee M. Decent direction methods on the feasible region recognized by supervised learning metamodels to solve unstructured problems. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2018. [DOI: 10.1080/02522667.2017.1324588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Shadi Abpeykar
- Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave., Tehran 15875-4413, Iran
| | - Mehdi Ghatee
- Department of Computer Science, Amirkabir University of Technology, N. 424, Hafez Ave., Tehran 15875-4413, Iran
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Han Z, Zhao J, Liu Q, Wang W. Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels. Inf Sci (N Y) 2016. [DOI: 10.1016/j.ins.2015.10.020] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Xu L, Qian F, Li Y, Li Q, Yang YW, Xu J. Resource allocation based on quantum particle swarm optimization and RBF neural network for overlay cognitive OFDM System. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.08.083] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Wang J, Song P, Wang Z, Zhang B, Liu W, Yu J. A Combined Model for Regional Eco-environmental Quality Evaluation Based on Particle Swarm Optimization–Radial Basis Function Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015. [DOI: 10.1007/s13369-015-1958-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Xiao C, Hao K, Ding Y. The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine. MATERIALS 2014; 8:117-136. [PMID: 28787927 PMCID: PMC5455220 DOI: 10.3390/ma8010117] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2014] [Accepted: 12/19/2014] [Indexed: 11/27/2022]
Abstract
This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.
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Affiliation(s)
- Chuncai Xiao
- College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
| | - Kuangrong Hao
- College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
- Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Shanghai 201620, China.
| | - Yongsheng Ding
- College of Information Sciences and Technology, Donghua University, Shanghai 201620, China.
- Engineering Research Center of Digitized Textile & Apparel Technology, Ministry of Education, Shanghai 201620, China.
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A multi-output two-stage locally regularized model construction method using the extreme learning machine. Neurocomputing 2014. [DOI: 10.1016/j.neucom.2013.03.056] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Zhang R, Tao J, Gao F. Temperature Modeling in a Coke Furnace with an Improved RNA-GA Based RBF Network. Ind Eng Chem Res 2014. [DOI: 10.1021/ie4027617] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Ridong Zhang
- Information
and Control Institute, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang, P R China
- Department
of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
| | - Jili Tao
- Ningbo
Institute of Technology, Zhejiang University, Ningbo, 315100 Zhejiang, P R China
| | - Furong Gao
- Department
of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
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Kayhan G, Ozdemir AE, Eminoglu İ. Reviewing and designing pre-processing units for RBF networks: initial structure identification and coarse-tuning of free parameters. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1053-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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13
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Luengo J, Herrera F. Shared domains of competence of approximate learning models using measures of separability of classes. Inf Sci (N Y) 2012. [DOI: 10.1016/j.ins.2011.09.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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