A Novel Approach for Blast-Induced Fly Rock Prediction Based on Particle Swarm Optimization and Artificial Neural Network

[1]  A. Gani,et al.  A clustering model based on an evolutionary algorithm for better energy use in crop production , 2015, Stochastic Environmental Research and Risk Assessment.

[2]  M. R. Moghaddam,et al.  Application of two intelligent systems in predicting environmental impacts of quarry blasting , 2015, Arabian Journal of Geosciences.

[3]  Ratnesh Trivedi,et al.  Prediction of Blast-Induced Flyrock in Opencast Mines Using ANN and ANFIS , 2015, Geotechnical and Geological Engineering.

[4]  Danial Jahed Armaghani,et al.  A Novel Approach for Blast-Induced Flyrock Prediction Based on Imperialist Competitive Algorithm and Artificial Neural Network , 2014, TheScientificWorldJournal.

[5]  Ratnesh Trivedi,et al.  APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR BLAST PERFORMANCE EVALUATION , 2014 .

[6]  Mansoureh Maadi,et al.  Optimization of Cluster Heads Selection by Imperialist Competitive Algorithm in Wireless Sensor Networks , 2014 .

[7]  Elahe Taherian Fard,et al.  A new hybrid imperialist competitive algorithm on data clustering , 2011 .

[8]  Siamak Talatahari,et al.  Optimum design of skeletal structures using imperialist competitive algorithm , 2010 .

[9]  Zahra Nasiri-Gheidari,et al.  Application of an imperialist competitive algorithm to the design of a linear induction motor , 2010 .

[10]  C. Kesselman,et al.  The Grid 2: Blueprint for a New Computing Infrastructure , 1998 .

[11]  S. Karami,et al.  Application of Imperialist Competitive Algorithm for Automated Classification of Remote Sensing Images , 2012 .

[12]  Li Xibing,et al.  Safety Evaluation of Blasting Flyrock Risk with FTA Method , 2011 .

[13]  M. Ghanavati Hybrid Imperialist Competitive Algorithm and Dynamic Validity Index to find the best clusters , 2011 .

[14]  M. R. Gholamian,et al.  Publication of Little Lion Scientific R & D , Islamabad PAKISTAN AN EFFICIENT COST FUNCTION FOR IMPERIALIST COMPETITIVE ALGORITHM TO FIND BEST CLUSTERS , 2011 .