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The vast majority of products and processes in industry and academia require human interaction. Thus, digital human models (DHMs) are becoming critical for improved designs, injury prevention, and a better understanding of human behavior. Although many capabilities in the DHM field continue to mature, there are still many opportunities for improvement, especially with respect to posture- and motion-prediction. Thus, this work investigates the use of artificial neural network (ANN) for improving predictive capabilities and for better understanding how and why human behave the way they do.
This discussion offers a systematic approach to predict the adsorption characteristics of a pharmaceutical pollutant, ibuprofen through artificial neural network. The artificial neural network is inspired by biological nervous system. Artificial neural networks are being extensively used for predicting the rate of adsorption of an adsorbent in solid-liquid adsorption system. Adsorption is a versatile method for the treatment of waste water bearing various pollutants. In case of batch adsorption study, the most significant output of an adsorption process, the adsorption capacity can be predicted either by equilibrium study or kinetic study. But application of a new method for the prediction of ibuprofen adsorption is artificial neural network which bifurcates the conventional prediction methods. In the present investigation, the ibuprofen adsorption capacity of microwave assisted activated carbon was predicted through artificial neural network.
An approach of Artificial Neural Network (ANN) is getting used to enhance the robot's capabilities in terms of autonomous decision making. The ANN helps in learning of robot, so that it performs motion independently based on it's learning and perform assigned tasks efficiently, specially in hazardous environment that is beyond human reach. These robots are used for espionage, space research, medical services, industrial automation and many other fields. With use of Artificial Neural Network their performance improves.
Artificial neural networks are suitable for many tasks in pattern recognition and machine learning. Unlike conventional techniques for time series analysis, an artificial neural network needs little information about the time series data and can be applied to a broad range of problems. The usage of artificial neural networks for time series analysis relies purely on the data that were observed. As Radial Basis networks with one hidden layer is capable of approximating any measurable function. An artificial neural network is powerful enough to represent any form of time series. The capability to generalize allows artificial neural networks to learn even in the case of noisy and/or missing data. Another advantage over linear models is the network's ability to represent nonlinear time series. Prediction of tides is very much essential for human activities and to reduce the construction cost in marine environment. This book presents an application of the artificial neural network with Radial basis function for accurate prediction of tides. This neural network model predicts the time series data of hourly tides directly while using an an efficient learning process.
This book proposes Artificial Neural Network (ANN) Applications for Smart grids and Energy Systems as a one of powerful artificial intelligence nonlinear regression techniques. This study is carried out to emphasize on the importance of ANN in many categories and for undergraduate, graduate students, engineers, and researchers. Artificial Neural Networks (ANNs) Technique is illustrated with its Fundamentals, Data Collection, Analysis and Processing, Structure Design, Number of Hidden Layers, Number of Hidden Units, Initializing Back-Propagation feed-forward network, Training, simulation, Weights and Bias, Testing, Derived mathematical equations and Graphical user interface. The adopted nonparametric ANN examples here are: Photovoltaic (PV) modeling, PM Synchronous m/c performance improvement, Storage Unit modeling, PV module Genetic Modeling, Petroleum Application for archie parameters estimation, dc-dc duty cycle Converter Estimation, Horizontal Axis Wind Turbines modeling and Capacitive Deionization (CDI) characteristics modeling for desalination application.
The present study explores the potential of artificial neural network to predict the performance and exhaust emissions of an existing single cylinder four-stroke CRDI engine under varying CNG and EGR strategies. Based on the experimental data an ANN model is developed to predict performance and emission parameters of the experimental engine. The study was carried out with 70% of total experimental data selected for training the neural network, 15% for the network’s cross-validation and remaining 15% data has been used for testing the performance of the trained network. The developed ANN models were capable of predicting the performance and emissions of the experimental engine with excellent agreement.
This book analyses the feasibility and the practicality of a structural damage detection system for bridges using artificial intelligence. The artificial intelligence used is known as an Artificial Neural Network, a computer program loosely based on the design of the human brain and known for its learning capability. Several damage cases are analysis and tested to train the Neural Network after which, given a set of inputs, it has the capability of predicting the state and location of the damage in a bridge deck. This book represents a perfect introduction to the basic theory and practicality of using Neural Networks in engineering applications. It furthers this concept by applying the mathematical theory into a feasible and practical tool for practicing engineers.
Continuous changing scenarios of software development technology make effort estimation more challenging. Some of the difficulties of estimation arise from the complexity and invisibility of software. Software development is intensively human activity and can’t be free from error. Ability of ANN(Artificial Neural Network) to model a complex set of relationship between the dependent variable (effort) and the independent variables (cost drivers) makes it as a potential tool for estimation. The application of artificial neural networks in prediction of effort in conventional and Object Oriented Software development approach has been discussed.
A highly secured network with improved network performance in transmission control protocol/internet protocol (TCP/IP) is the main aim of this research. The network for checking IP packet filtering rules was designed and analyzed for security aspects using expert system and neural networks. In traditional networks access control lists are processed sequentially. This degrades performance of the network since packets are analyzed one after another. To remedy this performance drawback, we addressed the problem from three different fronts: using an expert system, using parallel processing and using an artificial neural network implementation.
In this book the work is combination of mechanical has manufacturing & computer has networking. This two things are combined and Hybrid work is done with the help of Artificial Neural Network (ANN) in MATLAB. Artificial Neural Network is a mimicking tool of Human Brain. ANNs are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience. The fundamental processing element of a neural network is a neuron. This building block of human awareness encompasses a few general capabilities. Basically, a biological neuron receives inputs from other sources, combines them in some way, performs a generally nonlinear operation on the result, and then outputs the final result.
Selection of the topology of a neural network and correct parameters for the learning algorithm is a tedious task for designing an optimal artificial neural network, which is smaller, faster and with a better generalization performance. This book is my effort to mainly showcase the capability of time series prediction using neural network approach to predict complex Nonlinear datasets for forecasting. Experiments were carried out on Netflow series. It is observed that these problems exhibit a rich chaotic behavior and also leads to strange attractor. Multi-step ahead predictions have been carried out and the proposed neural network models have been optimized.
This book presents a new approach to analytically find a better way to reducing peak demand loads. A typical university campus loads are considered and artificial neural network is used in the training of the original university data to design a controller which helps reschedule loads based on the period of occupancy of each building on campus. Results obtained are compared to when this method is not applied and the difference in terms of the overall power consumed plus cost-difference favours the use of artificial neural network.
It is expected that neural network can make the computer intelligent. The cognitive tasks easily done by the animals can be possible by the neural network computing. Neural network is a different kind of computing where learning and adaptation are possible in the computer program. There are different appoaches for intelligence and neural network is one such method. Intelligence can be in many ways. It may be computational, decision making, classification etc. The learing and knowledge storing capabilities are the superior qualities of the neural network algorithms. Neural networks find application in almost all the field of enginnering. The prominent applications of neural networks are fault classification, system modelling and identification, prediction and forecasting, speech recognition, image recognition etc.
Artificial neural networks are made up of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.
In recent year, the concern in forecasting of economy and modeling of macroeconomic structure is increasing because of frequent economic crisis. This book explores how a researcher can use statistical forecasting model especially Artificial Neural Network (ANN) model in forecasting performance and modeling the macroeconomic indicator like GDP. This book is a wide-ranging and a practical guide to the use of time series modeling in forecasting purpose. If anyone is willing to be challenged about his/her current methodology and thinking, this book will be invaluable.