Enhanced artificial ecosystem-based optimization self-organizing RBF neural network

[1]  Zhenyu He,et al.  Learning Adaptive Spatial-Temporal Context-Aware Correlation Filters for UAV Tracking , 2022, ACM Trans. Multim. Comput. Commun. Appl..

[2]  Lukun Wang,et al.  RBF Neural Network-Based Supervisor Control for Maglev Vehicles on an Elastic Track With Network Time Delay , 2022, IEEE Transactions on Industrial Informatics.

[3]  Xiaojun Chang,et al.  Deep Discrete Cross-Modal Hashing with Multiple Supervision , 2021, Neurocomputing.

[4]  Ioannis G. Tsoulos,et al.  Train RBF networks with a hybrid genetic algorithm , 2021, Evolutionary Intelligence.

[5]  Dalia Yousri,et al.  Aquila Optimizer: A novel meta-heuristic optimization algorithm , 2021, Comput. Ind. Eng..

[6]  M. Abd Elaziz,et al.  Prediction of power consumption and water productivity of seawater greenhouse system using random vector functional link network integrated with artificial ecosystem-based optimization , 2020 .

[7]  N. Rajasekar,et al.  A novel objective function with artificial ecosystem-based optimization for relieving the mismatching power loss of large-scale photovoltaic array , 2020, Energy Conversion and Management.

[8]  Binh Thai Pham,et al.  Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping , 2020 .

[9]  Minnan Luo,et al.  Self-weighted Robust LDA for Multiclass Classification with Edge Classes , 2020, ACM Trans. Intell. Syst. Technol..

[10]  Saeid Barshandeh,et al.  HMPA: an innovative hybrid multi-population algorithm based on artificial ecosystem-based and Harris Hawks optimization algorithms for engineering problems , 2020, Engineering with Computers.

[11]  Zhile Yang,et al.  A novel competitive swarm optimized RBF neural network model for short-term solar power generation forecasting , 2020, Neurocomputing.

[12]  Mihailo Micev,et al.  RETRACTED: Artificial ecosystem-based optimization for optimal tuning of robust PID controllers in AVR systems with limited value of excitation voltage , 2020, The International Journal of Electrical Engineering & Education.

[13]  Yunhao Liu,et al.  Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition , 2020, IEEE Transactions on Cybernetics.

[14]  Qamar Askari,et al.  Political Optimizer: A novel socio-inspired meta-heuristic for global optimization , 2020, Knowl. Based Syst..

[15]  Lina Yao,et al.  A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[16]  Feiping Nie,et al.  Person Reidentification via Multi-Feature Fusion With Adaptive Graph Learning , 2020, IEEE Transactions on Neural Networks and Learning Systems.

[17]  Aníbal R. Figueiras-Vidal,et al.  Designing non-linear minimax and related discriminants by disjoint tangent configurations applied to RBF networks , 2020, Neurocomputing.

[18]  Chengye Li,et al.  Gaussian mutational chaotic fruit fly-built optimization and feature selection , 2020, Expert Syst. Appl..

[19]  Wei-guo Zhao,et al.  Artificial ecosystem-based optimization: a novel nature-inspired meta-heuristic algorithm , 2019, Neural Computing and Applications.

[20]  Nurettin Cetinkaya,et al.  A new meta-heuristic optimizer: Pathfinder algorithm , 2019, Appl. Soft Comput..

[21]  Xiaojun Chang,et al.  Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval , 2019, IEEE Transactions on Multimedia.

[22]  Zhihui Li,et al.  Zero-shot event detection via event-adaptive concept relevance mining , 2019, Pattern Recognit..

[23]  R. J. Kuo,et al.  Combining SOM and evolutionary computation algorithms for RBF neural network training , 2019, J. Intell. Manuf..

[24]  Yu Tian,et al.  Self-Organizing RBF Neural Network Using an Adaptive Gradient Multiobjective Particle Swarm Optimization , 2019, IEEE Transactions on Cybernetics.

[25]  Hossien Riahi-Madvar,et al.  Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry , 2019, Engineering Applications of Computational Fluid Mechanics.

[26]  Michael Granitzer,et al.  Sequence classification for credit-card fraud detection , 2018, Expert Syst. Appl..

[27]  Nicu Sebe,et al.  Dynamic Affinity Graph Construction for Spectral Clustering Using Multiple Features , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Feiping Nie,et al.  Rank-Constrained Spectral Clustering With Flexible Embedding , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[29]  Hossam Faris,et al.  Training radial basis function networks using biogeography-based optimizer , 2018, Neural Computing and Applications.

[30]  Jinkun Liu,et al.  An adaptive RBF neural network control method for a class of nonlinear systems , 2018, IEEE/CAA Journal of Automatica Sinica.

[31]  Qinghua Zheng,et al.  An Adaptive Semisupervised Feature Analysis for Video Semantic Recognition , 2018, IEEE Transactions on Cybernetics.

[32]  Rishabh Kumar,et al.  Credit card fraud detection using Naïve Bayes model based and KNN classifier , 2018 .

[33]  Hossam Faris,et al.  Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems , 2017, Adv. Eng. Softw..

[34]  Samuel A. Oluwadare,et al.  Credit card fraud detection using machine learning techniques: A comparative analysis , 2017, 2017 International Conference on Computing Networking and Informatics (ICCNI).

[35]  Jie Zhang,et al.  A novel chaos optimization algorithm , 2017, Multimedia Tools and Applications.

[36]  Yu Gong,et al.  A Fast Adaptive Tunable RBF Network For Nonstationary Systems , 2016, IEEE Transactions on Cybernetics.

[37]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[38]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[39]  Reid A. Johnson,et al.  Calibrating Probability with Undersampling for Unbalanced Classification , 2015, 2015 IEEE Symposium Series on Computational Intelligence.

[40]  Dinesh Kumar,et al.  Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems , 2014, J. Comput. Sci..

[41]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[42]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[43]  Yue-Shi Lee,et al.  Cluster-based under-sampling approaches for imbalanced data distributions , 2009, Expert Syst. Appl..

[44]  Michel Verleysen,et al.  K nearest neighbours with mutual information for simultaneous classification and missing data imputation , 2009, Neurocomputing.

[45]  Aihua Shen,et al.  Application of Classification Models on Credit Card Fraud Detection , 2007, 2007 International Conference on Service Systems and Service Management.

[46]  Bing Yu,et al.  Training radial basis function networks with differential evolution , 2006, 2006 IEEE International Conference on Granular Computing.

[47]  Yu Liu,et al.  Training Radial Basis Function Networks with Particle Swarms , 2004, ISNN.

[48]  Ruisheng Zhang,et al.  Prediction of Enthalpy of Alkanes by the Use of Radial Basis Function Neural Networks , 2001, Comput. Chem..

[49]  Friedhelm Schwenker,et al.  Three learning phases for radial-basis-function networks , 2001, Neural Networks.

[50]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[51]  William Nick Street,et al.  Breast Cancer Diagnosis and Prognosis Via Linear Programming , 1995, Oper. Res..

[52]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[53]  Al-Attar Ali Mohamed,et al.  Optimal power flow using moth swarm algorithm , 2017 .

[54]  Hossam Faris,et al.  Chapter 28 – Evolving Radial Basis Function Networks Using Moth–Flame Optimizer , 2017 .

[55]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[56]  J. Townsend.  Theoretical analysis of an alphabetic confusion matrix , 1971 .