Journal ArticleDOI
Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
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TLDR
A new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting is presented and it is suggested that the PSO-based ANN model outperforms the other predictive models.About:
This article is published in Applied Acoustics.The article was published on 2014-06-01. It has received 175 citations till now.read more
Citations
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Journal ArticleDOI
Prediction of seismic slope stability through combination of particle swarm optimization and neural network
TL;DR: It was found that the PSO–ANN technique can predict FOS with higher performance capacities compared to ANN and R2 values of testing datasets equal to 0.915 and 0.986 suggest the superiority of thePSO– ANN technique.
Journal ArticleDOI
Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
TL;DR: In this article, a hybrid artificial neural network (ANN) optimized by the imperialist competitive algorithm (ICA) was proposed to predict peak particle velocity (PPV) resulting from quarry blasting.
Journal ArticleDOI
Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling
TL;DR: The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results, and the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.
Journal ArticleDOI
Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
Edy Tonnizam Mohamad,Danial Jahed Armaghani,Ehsan Momeni,Seyed Vahid Alavi Nezhad Khalil Abad +3 more
TL;DR: The high performance indices of the proposed model highlight the superiority of the PSO-based ANN model for UCS prediction, which is widely accepted that optimization algorithms such as particle swarm optimization can improve ANN performance.
Journal ArticleDOI
An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite
Danial Jahed Armaghani,Edy Tonnizam Mohamad,Ehsan Momeni,Mogana Sundaram Narayanasamy,Mohd For Mohd Amin +4 more
TL;DR: This study aims to present two predictive models of UCS and E for granite using an adaptive neuro-fuzzy inference system (ANFIS) and found that the ANFIS predictive model of UCS, with R2, RMSE and VAF equal to 0.2, outperforms the MRA and ANN models.
References
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Proceedings ArticleDOI
Particle swarm optimization
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Journal ArticleDOI
A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Proceedings ArticleDOI
A modified particle swarm optimizer
Yuhui Shi,Russell C. Eberhart +1 more
TL;DR: A new parameter, called inertia weight, is introduced into the original particle swarm optimizer, which resembles a school of flying birds since it adjusts its flying according to its own flying experience and its companions' flying experience.
Journal ArticleDOI
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
M. Clerc,James Kennedy +1 more
TL;DR: This paper analyzes a particle's trajectory as it moves in discrete time, then progresses to the view of it in continuous time, leading to a generalized model of the algorithm, containing a set of coefficients to control the system's convergence tendencies.
Book
neural networks and fuzzy systems a dynamical systems approach to machine intelligence
TL;DR: This work combines neural networks and fuzzy systems, presenting neural networks as trainable dynamical systems and developing mechanisms and principles of adaption, self-organization, convergence and global stability.