A Fuzzy Gaussian Clifford Neural Network

Iatan, Iuliana F.

doi:10.1007/978-3-319-43871-9_5

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…As shown in figure 2: In order to test the validity of our model, we use the fuzzy neural network [10] and wavelet neural network [11] to compare the two neural network methods. PSO gradient descent algorithm based on SVM energy consumption model, as shown in figure 3: Fuzzy prediction results shown in Figure 4, Figure 5 shows the neural network and wavelet neural network, the prediction accuracy is compared with the SVM model and PSO based on the gradient descent algorithm, the variance of M were 0.2243, 0.3135 higher than the mean variance model algorithm, thus effectively prove the accuracy and feasibility of the proposed regression model.…”

Section: Simulation Experimentsmentioning

confidence: 99%

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Sun¹,

Ren²,

Ye³

et al. 2017

dtcse

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Abstract. Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.

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Section: Simulation Experimentsmentioning

confidence: 99%

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Sun¹,

Ren²,

Ye³

et al. 2017

dtcse

View full text Add to dashboard Cite

show abstract

“…13 In order to achieve the aim of illustrating the usefulness of the Clifford algebra in the neural computing because of its geometric properties, we have introduced in Chap. 5, the Fuzzy Clifford Gaussian network (FCGNN), contributing [15] in this way to continue the development of neural networks in other than the real domain.…”

Section: Introductionmentioning

confidence: 99%

Issues in the Use of Neural Networks in Information Retrieval

Iatan

2017

Studies in Computational Intelligence

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The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the worldwide distribution, which enable both wide and rapid dissemination of research output.

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A Fuzzy Gaussian Clifford Neural Network

Cited by 2 publications

References 4 publications

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Issues in the Use of Neural Networks in Information Retrieval

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