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Author: Pooria Mazaheri Publisher: ISBN: Category : Computers Languages : en Pages : 0
Book Description
Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters.
Author: Pooria Mazaheri Publisher: ISBN: Category : Computers Languages : en Pages : 0
Book Description
Neural network modeling has become a special interest for many engineers and scientists to be utilized in different types of data as time series, regression, and classification and have been used to solve complicated practical problems in different areas, such as medicine, engineering, manufacturing, military, business. To utilize a prediction model that is based upon artificial neural network (ANN), some challenges should be addressed that optimal designing and training of ANN are major ones. ANN can be defined as an optimization task because it has many hyper parameters and weights that can be optimized. Metaheuristic algorithms such as swarm intelligence-based methods are a category of optimization methods that aim to find an optimal structure of ANN and to train the network by optimizing the weights. One of the commonly used swarm intelligence-based algorithms is particle swarm optimization (PSO) that can be used for optimizing ANN. In this study, we review the conducted research works on optimizing the ANNs using PSO. All studies are reviewed from two different perspectives: optimization of weights and optimization of structure and hyper parameters.
Author: Satchidananda Dehuri Publisher: World Scientific ISBN: 9814280143 Category : Computers Languages : en Pages : 352
Book Description
This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning. To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.
Author: Ardashir Mohammadazadeh Publisher: Springer Nature ISBN: 3031145712 Category : Technology & Engineering Languages : en Pages : 124
Book Description
This book explains the basic concepts, theory and applications of neural networks in a simple unified approach with clear examples and simulations in the MATLAB programming language. The scripts herein are coded for general purposes to be easily extended to a variety of problems in different areas of application. They are vectorized and optimized to run faster and be applicable to high-dimensional engineering problems. This book will serve as a main reference for graduate and undergraduate courses in neural networks and applications. This book will also serve as a main basis for researchers dealing with complex problems that require neural networks for finding good solutions in areas, such as time series prediction, intelligent control and identification. In addition, the problem of designing neural network by using metaheuristics, such as the genetic algorithms and particle swarm optimization, with one objective and with multiple objectives, is presented.
Author: Satchidananda Dehuri Publisher: World Scientific ISBN: 9814467316 Category : Computers Languages : en Pages : 352
Book Description
This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning.To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.
Author: Parsopoulos, Konstantinos E. Publisher: IGI Global ISBN: 1615206671 Category : Business & Economics Languages : en Pages : 328
Book Description
"This book presents the most recent and established developments of Particle swarm optimization (PSO) within a unified framework by noted researchers in the field"--Provided by publisher.
Author: Preethi Gunishetty Devarakonda Publisher: ISBN: Category : Computers Languages : en Pages : 0
Book Description
The increased usage of smartphones for daily activities has created a huge demand and opportunities in the field of ubiquitous computing to provide personalized services and support to the user. In this aspect, Sensor-Based Human Activity Recognition (HAR) has seen an immense growth in the last decade playing a major role in the field of pervasive computing by detecting the activity performed by the user. Thus, accurate prediction of user activity can be valuable input to several applications like health monitoring systems, wellness and fit tracking, emergency communication systems etc., Thus, the current research performs Human Activity Recognition using a Particle Swarm Optimization (PSO) based Convolutional Neural Network which converges faster and searches the best CNN architecture. Using PSO for the training process, intends to optimize the results of the solution vectors on CNN which in turn improve the classification accuracy to reach the quality performance compared to the state-of-the-art designs. The study investigates the performances of PSO-CNN algorithm and compared with that of classical machine leaning algorithms and deep learning algorithms. The experiment results showed that the PSO-CNN algorithm was able to achieve the performance almost equal to the state-of-the-art designs with a accuracy of 93.64%. Among machine learning algorithms, Support Vector machine found to be best classifier with accuracy of 95.05% and a Deep CNN model achieved 92.64% accuracy score.
Author: Abhishek Sharma Publisher: CRC Press ISBN: 1000529754 Category : Technology & Engineering Languages : en Pages : 152
Book Description
Swarm intelligence is one of the fastest growing subfields of artificial intelligence and soft computing. This field includes multiple optimization algorithms to solve NP-hard problems for which conventional methods are not effective. It inspires researchers in engineering sciences to learn theories from nature and incorporate them. Swarm Intelligence: Foundation, Principles, and Engineering Applications provides a comprehensive review of new swarm intelligence techniques and offers practical implementation of Particle Swarm Optimization (PSO) with MATLAB code. The book discusses the statistical analysis of swarm optimization techniques so that researchers can analyse their experiment design. It also includes algorithms in social sectors, oil and gas industries, and recent research findings of new optimization algorithms in the field of engineering describing the implementation in machine learning. This book is written for students of engineering, research scientists, and academicians involved in the engineering sciences.
Author: Thomas, J. Joshua Publisher: IGI Global ISBN: 1799836460 Category : Technology & Engineering Languages : en Pages : 520
Book Description
Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.
Author: Pooria Mazaheri
Author: Pooria Mazaheri
Author: Satchidananda Dehuri
Author: Ardashir Mohammadazadeh
Author: Satchidananda Dehuri
Author: Parsopoulos, Konstantinos E.
Author: Preethi Gunishetty Devarakonda
Author: Arnold Kaufmann
Author: Abhishek Sharma
Author: Ali Kaveh
Author: Thomas, J. Joshua