Fuzzy Restricted Boltzmann Machine Research Articles

A Novel Energy Accounting Model Using Fuzzy Restricted Boltzmann Machine—Recurrent Neural Network

Energy accounting is a system for regularly measuring, analyzing, and reporting the energy use of various activities. This is done to increase energy efficiency and monitor the impact of energy usage on the environment. Primary energy accounting is now done by determining the amount of fossil fuel energy required to generate it. However, if fossil fuels become scarcer, this strategy becomes less viable. Instead, a new energy accounting approach will be required, one that takes into consideration the intermittent character of the two most prevalent renewable energy sources, wind and solar power. Furthermore, estimation of the energy consumption data collected from household surveys, whether using a recall-based approach or a meter-based one, remains a difficult task. Hence, this paper proposes a novel energy accounting model using Fuzzy Restricted Boltzmann Machine-Recurrent Neural Network (FRBM-RNN). The energy consumption dataset is preprocessed using linear-scaling normalization. The proposed model is optimized using the Adaptive Fuzzy Adam Optimization Algorithm (AFAOA). The performance metrics like Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are estimated. The estimated results for our proposed technique are MSE (0.19), RMSE (0.44), MAE (0.2), and MAPE (3.5).

Blockchain-Assisted Vehicle and Cargo Matching Using Optimal Fuzzy Restricted Boltzmann Machine in Autonomous Transport System

Blockchain-Assisted Vehicle and Cargo Matching Using Optimal Fuzzy Restricted Boltzmann Machine in Autonomous Transport System

Image feature extraction based on fuzzy restricted Boltzmann machine

In order to accelerate the speed of visual image feature extraction and solve the problem of training deceleration caused by the introduction of fuzzy numbers into the original Restricted Boltzmann Machine (RBM), a Mixed Accelerated learning method based on a Fuzzy Restricted Boltzmann Machine (MAFRBM) is studied. Firstly, the penalty factor is introduced to increase the sparsity of data and improve the effectiveness of feature information extraction. Secondly, the characteristic similarity of the hidden layer units of the network is compared. When the unit similarity is higher than the threshold, the unit is a redundant unit for repeated learning, which is eliminated to accelerate learning. Thirdly, the reconstruction error of each training is used to adaptively change the network learning rate to achieve further acceleration. Finally, compared with the change of network reconstruction error, the accuracy of network convergence is defined to accelerate learning convergence. And a multi-functional image recognition model is constructed, which is MAFRBM + SVM. The features extracted by MAFRBM unsupervised learning are imported into SVM to test the effectiveness and practicability of MAFRBM. The experimental results show that the learning speed and feature extraction ability of MAFRBM are better than other models.

Extractive text summarization using F-RBM

The Intense growth of data over the internet and process extracting predominating details from it is a time-consuming task. A new application of making this task simplified is text-summarization. This paper presents the integration of Restricted Boltzmann Machine and Fuzzy Logic methods. Both of these processes have different ways of giving precision to a summary but together being part of the unsupervised world has surpassed their working alone to summarize the text. Integrated algorithm i.e. called FRBM (fuzzy Restricted Boltzmann Machine) provides enhanced depiction in the modeling of probability over visible and hidden units than the RBM.

FuRL: fuzzy RBM learning framework to detect and mitigate network anomalies in Information Centric Network

Information Centric Network (ICN) is a promising next-generation internet architecture in which the network focuses on retrieving the content by employing open in-network caching scheme to provide an efficient content distribution to users. However, such open in-network caching is vulnerable to network anomalies. In particular, cache pollution attack disrupts the smooth working of in-network caching by flooding unpopular contents. Hence, the in-network caching malfunctions and legitimate consumer requests are dropped. To address this problem, a secure framework based on Fuzzy Restricted Boltzmann Machine has been proposed to detect the anomalies and defend against such pollution attacks in ICN. Further, a reward-based cache replacement (ReBac) algorithm that is capable of avoiding cache pollution attack has also been proposed. The experimental results obtained while testing the proposed framework show better detection rate compared with the state-of-art solution and the proposed framework shows better cache rate.

Interval Type-2 Fuzzy Restricted Boltzmann Machine

Restricted Boltzmann machine (RBM) is an energy-based artificial neural network (ANN), applied in several applications like image processing, topic modeling, classification, regression, and pattern recognition. The fuzzy version of RBM is a new approach in this field, with parameters considered as fuzzy numbers. In this article, a fuzzy RBM is extended through interval type-2 membership functions, named the interval type-2 fuzzy RBM (IT2FRBM). The additional uncertainties in the structures of the membership functions are embedded in this model. This is formulated as a maximum likelihood problem which allows the parameters of the type-2 fuzzy numbers to be learned. The capabilities of this proposed approach as a discriminative or generative model are assessed. The robustness of this method against noise is analyzed. The results indicate that this IT2FRBM outperforms RBM and its different fuzzy versions.

Generative and Discriminative Fuzzy Restricted Boltzmann Machine Learning for Text and Image Classification.

The restricted Boltzmann machine (RBM) is an excellent generative learning model for feature extraction. By extending its parameters from real numbers to fuzzy ones, we have developed the fuzzy RBM (FRBM) which is demonstrated to possess better generative capability than RBM. In this paper, we first propose a generative model named Gaussian FRBM (GFRBM) to deal with real-valued inputs. Then, motivated by the fact that the discriminative variant of RBM can provide a self-contained framework for classification with competitive performance compared with some traditional classifiers, we establish the discriminative FRBM (DFRBM) and discriminative GFRBM (DGFRBM) that combine both the generative and discriminative facility by adding extra neurons next to the input units. Specifically, they can be trained into excellent stand-alone classifiers and retain outstanding generative capability simultaneously. The experimental results including text and image (both clean and noisy) classification indicate that DFRBM and DGFRBM outperform discriminative RBM models in terms of reconstruction and classification accuracy, and they behave more stable when encountering noisy data. Moreover, the proposed learning models show some promising advantages over other standard classifiers.

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on the Crisp Possibilistic Mean Value of Fuzzy Numbers

A fuzzy restricted Boltzmann machine (FRBM) is extended from a restricted Boltzmann machine (RBM) by replacing all the real-valued parameters with fuzzy numbers. A new FRBM that employs the crisp possibilistic mean value of a fuzzy number to defuzzify the fuzzy free energy function is presented. This approach is much clearer and easier to obtain the expression of the defuzzified free energy function and its approximation than the centroid method. Several theorems that discuss the error bounds of the approximation to ensure the rationality and validity are also investigated. Learning algorithms are given for the designed FRBM with symmetric triangular fuzzy numbers (STFNs), asymmetric triangular fuzzy numbers, and Gaussian fuzzy numbers. By appropriately choosing the parameters, a theorem is concluded that all FRBMs with symmetric fuzzy numbers will have identical learning algorithm to that of FRBMs with STFNs. This is illustrated by a case of FRBM with Gaussian fuzzy numbers. Two experiments including the MNIST handwriting recognition and the Bars-and-Stripes benchmark are carried out. The results show that the proposed FRBMs significantly outperform RBMs in learning accuracy and generalization ability, especially when encountering unlearned samples and recovering incomplete images.

Tolerance of intrinsic device variation in fuzzy restricted Boltzmann machine network based on memristive nano-synapses

Inspired by the architecture and principle of the human brain, neuromorphic computing has attracted enormous research interest due to its potential for massively parallel and energy-efficient computing, where nanoscale memristors are considered as perfect building blocks for hardware neural networks, serving as compact, analog synapses. However, the inherent variation in memristors has been regarded as a major obstacle to their practical application in neuromorphic computing. Here, for the first time, we demonstrate that this long-standing issue can be addressed by introducing fuzziness into the neural networks. We found that the cycle-to-cycle and device-to-device conductance variations in the on and off states of Pt/TaOx/Ta memristors statistically follow Gaussian distributions, and using an experimentally verified compact synapse model based on the electrical characteristics of Pt/TaOx/Ta devices, a fuzzy restricted Boltzmann machine (FRBM) network was constructed where all the weight states were fuzzified to accommodate device stochasticity. The FRBM network has shown significantly improved tolerance to device variation, as confirmed by increased accuracy in the benchmark test of MNIST handwritten digit classifications. This study thus provides a new route towards highly robust neuromorphic computing, even if the computing elements can be stochastic and inhomogeneous.

Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

In recent years, deep learning caves out a research wave in machine learning. With outstanding performance, more and more applications of deep learning in pattern recognition, image recognition, speech recognition, and video processing have been developed. Restricted Boltzmann machine (RBM) plays an important role in current deep learning techniques, as most of existing deep networks are based on or related to it. For regular RBM, the relationships between visible units and hidden units are restricted to be constants. This restriction will certainly downgrade the representation capability of the RBM. To avoid this flaw and enhance deep learning capability, the fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers. This way, the original RBM becomes a special case in the FRBM, when there is no fuzziness in the FRBM model. In the process of learning FRBM, the fuzzy free energy function is defuzzified before the probability is defined. The experimental results based on bar-and-stripe benchmark inpainting and MNIST handwritten digits classification problems show that the representation capability of FRBM model is significantly better than the traditional RBM. Additionally, the FRBM also reveals better robustness property compared with RBM when the training data are contaminated by noises.