Method of Artificial Neural Networks Teaching
10 May 2020-Vol. 17, Iss: 1, pp 43-64
TL;DR: It is established that the mentioned training method provides on average 16-23 percent higher efficiency of training of artificial neural networks and does not accumulate errors during training.
Abstract: The technique of artificial neural networks training has been developed. A distinctive feature of the proposed technique is that it provides training not only for the synaptic weights of the artificial neural network but also for the type and parameters of the membership function. If it is impossible to provide, the specified quality of functioning of artificial neural networks due to the learning of the parameters of the artificial neural network, the architecture of artificial neural networks is trained. The choice of architecture, type and parameters of the membership function takes into account the computing resources of the tool and taking into account the type and amount of information that is coming to the input of an artificial neural network. Also, while using the proposed method, there is no accumulation of error learning artificial neural networks as a result of processing information, which is supplied to the input of artificial neural networks. The development of the proposed methodology is due to the need to train artificial neural networks, in order to process more information, with the uniqueness of the made decisions. According to the results of the research, it is established that the mentioned training method provides on average 16-23 percent higher efficiency of training of artificial neural networks and does not accumulate errors during training. This technique will allow to train artificial neural networks; identify effective measures to improve the performance of artificial neural networks. Also, the developed technique will increase the efficiency of the functioning of artificial neural networks by learning the parameters and architecture of artificial neural networks. The technique proposed by the authors reduces the use of computing resources for support and decision-making systems. Using the developed methodology will develop measures that are aimed at improving the efficiency of artificial neural networks training and increase the speed of the processing information.
Citations
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01 Jan 2002
TL;DR: This chapter contains sections titled: Historical Review Supervised Multilayer Networks unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation and Recommended Exercises.
Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises
452 citations
01 Jan 2019
TL;DR: Just how the invention of computers and the Internet has fundamentally changed the authors' world, machine learning is suddenly enabling analytics to be almost everywhere, and humans sometimes need to take a step back and take a deep breath in order to keep things in perspective.
Abstract: Just how the invention of computers and the Internet has fundamentally changed our world, machine learning is suddenly enabling analytics to be almost everywhere. Where such rapid change occurs, we humans are of course also prone to exuberance, even hype, and we sometimes need to take a step back and take a deep breath in order to keep things in perspective.
18 citations
DOI•
16 Aug 2021TL;DR: It is hoped that prosecution discretion by the prosecutor can reach to the monitoring of suspicious "nodes" and monitoring the registration of ICTs that are vulnerable to cryptocurrency crimes, in providing a deterrent effect to the perpetrators of cryptocurrency crime.
Abstract: Data, reports, and information show that cryptocurrency has supported certain parties as a convenience, whereas the purpose of cryptocurrency is to minimize the weaknesses of conventional money systems in international relations in the current era of globalization. Countries that cannot represent or apply autonomous law in facing cryptocurrency challenges, because it is feared it is increasingly difficult to overcome global cryptocurrency crime. It is precisely in eradicating cryptocurrency crime, law enforcement authorities, priorities of prosecutors who have the highest supremacy in the field of prosecution and other discretion in law enforcement must be dynamic in law enforcement against facilitators from responses to social needs and aspirations, in accordance with legal considerations must acknowledge the wishes of the community and agree in achieving substantive justice. Considering that virtual currency has been banned in Indonesia but crypto-asset trading on the futures exchange has been in force, responsive discretionary prosecutions are needed in combating cryptocurrency crime in Indonesia. Liability that exceeds liability based on faults, namely strict liability, vicarious liability, and secondary liability to any parties that cause cryptocurrency crime can be applied to the mechanism of "follow the money" and "trace the information and communication technologies (ICTs) footprint". It is hoped that prosecution discretion by the prosecutor can reach to the monitoring of suspicious "nodes" and monitoring the registration of ICTs that are vulnerable to cryptocurrency crimes, such as laptops, cellphones, computers, and SIM cards, in providing a deterrent effect to the perpetrators of cryptocurrency crime.
4 citations
TL;DR: A dynamical alert for thought and politics teaching based on the Long-Short-Term Memory Neural Network (LSTM), which uses a powerful global optimization function to optimize the parameters of the deep LSTM neural network and can predict students’ subject performance more accurately and has certain validity and feasibility.
Abstract: To strengthen and develop the thought and politics work in state-owned schools, schools must explore the theoretical system of thought and politics construction in the practice of scientific development ideas guiding school development, actively innovate the practice model and implement early warning management for thought and politics projects. This is because only by accurately analyzing the problems and causes can we seek more reasonable measures for the laws of thought and politics practice education for modern students. At present, promoting the deep integration of thought and politics teaching projects with information technology has become an important means of thought and politics teaching projects in schools. However, with the explosive growth of network data, the structure becomes more and more complex, and learners face the problem of information overload as more and more information overflows in the network environment. Precise support for students with learning disabilities is a research direction for precision thinking education, and most existing support strategies in schools include manual statistics of failed subjects, written warnings, or corrective measures through simple correlation algorithms. In this paper, we propose a dynamical alert for thought and politics teaching based on the Long-Short-Term Memory Neural Network (LSTM), which uses a powerful global optimization function to optimize the parameters of the deep LSTM neural network. The experimental results show that the average execution time of LSTM is 19.46 seconds and 8.24 seconds lower than that of SCB-DBSCAN and CFSFDP, respectively, which shows that the execution time of the LSTM algorithm is faster and more accurate. Therefore, the LSTM algorithm is feasible and effective. The LSTM-based dynamic warning of thought and politics teaching can predict students’ subject performance more accurately and has certain validity and feasibility.
1 citations
TL;DR: In the context of the big data era, a model for mining Civics elements using convolutional neural networks using A-Softmax algorithm and softmax output layer fusion is proposed, and the experiment proves that the improved model has a certain degree of improvement in performance.
Abstract: In the context of the big data era, a model for mining Civics elements using convolutional neural networks is proposed to address the problems of poor interaction between teaching practice and Civics elements. The use of this model for the mining and teaching practice of Civics elements allows teachers to make changes to the design of teaching contents in real time in order to maximize the integration of lecture contents and Civics elements. In addition, in order to improve the effectiveness of the model, an improved model with A-Softmax algorithm and softmax output layer fusion is proposed, and the experiment proves that the improved model has a certain degree of improvement in performance.
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"Method of Artificial Neural Network..." refers background in this paper
...When traditional Gaussians are used as a function of belonging (9), the corresponding gradient formulas of the objective function (12) for one pair of training data takes the form (Sugeno and Kang, 1998)...
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...(15) When traditional Gaussians are used as a function of belonging (9), the corresponding gradient formulas of the objective function (12) for one pair of training data takes the form (Sugeno and Kang, 1998)...
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TL;DR: Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy.
Abstract: Fuzzy systems are represented as series expansions of fuzzy basis functions which are algebraic superpositions of fuzzy membership functions. Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, an orthogonal least-squares (OLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs; then, the OLS algorithm is used to select significant fuzzy basis functions which are used to construct the final fuzzy system. The fuzzy basis function expansion is used to approximate a controller for the nonlinear ball and beam system, and the simulation results show that the control performance is improved by incorporating some common-sense fuzzy control rules. >
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TL;DR: Referring to the above said paper by Narendra-Parthasarathy (ibid.
Abstract: Referring to the above said paper by Narendra-Parthasarathy (ibid., vol.1, p4-27 (1990)), it is noted that the given Example 2 (p.15) has a third equilibrium state corresponding to the point (0.5, 0.5).
1,528 citations
"Method of Artificial Neural Network..." refers background in this paper
...(24) ‒ a learning algorithm that has tracking and smoothing properties (Narendra and Parthasarathy, 1990)...
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01 Jan 2002
TL;DR: This chapter contains sections titled: Historical Review Supervised Multilayer Networks unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation and Recommended Exercises.
Abstract: This chapter contains sections titled: Historical Review Supervised Multilayer Networks Unsupervised Neural Networks: Kohonen Network Unsupervised Networks: Adaptive Resonance Theory Network Model Validation Summary References Recommended Exercises
452 citations
"Method of Artificial Neural Network..." refers methods in this paper
...…realizes nonlinear mapping of the input space into a scalar output signal 1nR R , which is similar to a normalized RBFN (Haykin, 1999), and in architecture (at a fixed h) coincides with a TSK system (Takagi, Sugeno, Kang) of zero-order, that is called Wang-Mendel architecture (Nelles, 2001)....
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