An educational tool for artificial neural networks
TL;DR: An educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described and the whole tool environment provides an advanced system to ensure mentioned functions.
About: This article is published in Computers & Electrical Engineering.The article was published on 2011-05-01. It has received 32 citations till now. The article focuses on the topics: Time delay neural network & Deep learning.
Citations
More filters
TL;DR: A comparative study of different wavelet families for analysis of wrist motions from electromyography (EMG) signals finds that 'Biorthogonal' and 'Coiflets' wavelets families are more suitable for accurate classification of EMG signals of different wrist motions.
39 citations
Cites background from "An educational tool for artificial ..."
...ANNs provide alternative form of computing that attempts to mimic the functionality of the brain [26,36]....
[...]
TL;DR: This work introduces a novel methodology that merges singular value decomposition, statistical analysis, and artificial neural networks for multiple combined fault identification and demonstrates its high effectiveness on detecting faulty bearings, unbalance, broken rotor bars, and all their possible combinations.
39 citations
TL;DR: A real-time fuel cell emulator based on the Real-Time Windows Target from Matlab and a controlled power source is proposed, which accurately reproduces both static and dynamic fuel cell behaviors and can be used to design and test devices prior to their connection to real fuel cells, preventing undesired phenomena.
36 citations
Cites background from "An educational tool for artificial ..."
...In this way, the adoption of a Matlab-based system is also useful for educational purposes since Matlab is a widely accepted platform for design educational tools [16], therefore the FC emulator can be integrated in students projects or laboratory practices....
[...]
TL;DR: The paper provides a list of ANNs used to improve the ASD-control, extending the IM-driver life and achieving proper motor operation, their size and performance, and an overview of different ANN-based drive approaches.
29 citations
TL;DR: Self-organizing feature map in conjunction with radial basis function has been applied in this paper to determine and classify the voltage stability states of a multi-bus power network and can be considered an effective soft-computing tool to ease the operation of large-multi bus power network under variable operating conditions.
25 citations
References
More filters
Book•
01 Jan 1988TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Abstract: Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
37,989 citations
01 Jan 1988
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
17,604 citations
17 Jul 1986
15,313 citations
Book•
03 Jan 1986
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Abstract: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion
13,579 citations
Book•
[...]
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
Abstract: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981. Its theory and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it. The most important practical applications are in exploratory data analysis, pattern recognition, speech analysis, robotics, industrial and medical diagnostics, instrumentation, and control, and literally hundreds of other tasks. In this monograph the mathematical preliminaries, background, basic ideas, and implications are expounded in a manner which is accessible without prior expert knowledge.
12,920 citations