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A Novel Neural Network Based on Quantum Computing

INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS 2020年第7期59卷 2029-2043页

作者： Chen, Bu-Qing Niu, Xu-Feng PLA Informat Engn Univ Teaching & Res Sect Polit Luoyang 471003 Henan Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710121 Shaanxi Peoples R China

Since the first quantum neural network based on quantum computing was proposed by famous scholar Kak, much attention has been taken focus on designing new quantum neural network models. In this paper, a novel efficient quantum feed-forward neural network based on quantum computing is established, which adopts genetic algorithm to improve the traditional back propagation algorithm in parameters learning process. We clearly show the mathematical process of the new proposed quantum network model and improved algorithm. The experimental results of MATLAB simulations show that the new network model which makes the best use of fast quantum neural computation does a better job in function approximation and prediction of educational short video's spreading capacity than traditional back propagation neural network, and the improved algorithm is more efficient than common back propagation algorithm in the proposed quantum network model. Our model can be widely used in weather prediction, handwriting recognition, speech recognition, and other aspects.

关键词： Neural network Quantum computing Genetic algorithm back propagation algorithm

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Application of an emotional neural network to facial recognition

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NEURAL COMPUTING & APPLICATIONS 2009年第4期18卷 309-320页

作者： Khashman, Adnan Near East Univ ISRG Dept Elect & Elect Engn TR-10 Lefkosa Mersin Turkey

In our attempts to model human intelligence by mimicking the brain structure and function, we overlook an important aspect in human cognition, which is the emotional factor. It may currently sound unthinkable to have emotional machines;however, it is possible to simulate certain artificial emotions with the aim of improving machine learning. This paper investigates the efficiency of an emotional neural network, which uses a modified back propagation learning algorithm. The modifed algorithm, namely the emotional BP learning algorithm, has two emotional parameters, anxiety and confidence, that are modeled during machine learning and decision making. The emotional neural network will be implemented to a facial recognition problem using images of faces with different orientations and contrast levels, and its performance will be compared to that of a conventional neural network. Experimental results suggest that artificial emotions can be successfully modeled and efficiciently implemented to improve neural networks learning and generaliztion.

关键词： Neural networks back propagation algorithm Artificial emotion modeling Facial recognition

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Thermal Neutron Classification in the Hohlraum Using Artificial Neural Networks

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ENGINEERING LETTERS 2015年第2期23卷 87-91页

作者： Rivero, Jesus E. Valdovinos, Rosa M. Herrera, Edgar Montes-Venegas, Hector A. Alejo, Roberto Ctr Univ UAEM Texcoco El Tejocote 56259 Texcoco Mexico Univ Autonoma Estado Mexico Fac Ingn Toluca 50130 Mexico Tecnol Estudios Super Jocotitlan Jocotitlan 50700 Mexico Inst Nacl Invest Nucl ININ Dept Reactor La Marquesa 52750 Ocoyoacac Mexico

One potential method for the treatment of brain tumors is the use of thermal nuetrons flux. These type of neutrons are produced by the nuclear fission of uranium-235. When the nuclear fission occurs at the core of a reactor, part of the thermal neutrons flux go into the Hohlraum generating an isotopic expansion. To successfully carry out this radiation technique, it is necessary to evaluate the performance of the thermal neutrons flux at the Hohlraum during he isotropic expansion. In this paper, the classification of the resultant flux is achieved using a Multilayer Perceptron Artificial Neural Network (ANN). For the ANN topology configuration, a set of parameters specific to the structure of the horizontal thermal column of the Hohlraum reactor have been determined. Experimental results show the viability of the model and provide additional support to the reactor facilities remodeling tasks.

关键词： Artificial Neural Networks Multilayer perceptron back propagation algorithm Thermal neutrons

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To solve multi-class pattern classification problems by grid neural network

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2022年第2期15卷 183-197页

作者： Kumar, Ajendra Singh, Preet Pal Sharma, Dipa Joshi, Pawan Gurukula Kangri Deemed Univ Dept Math & Stat Haridwar 249404 Uttrakhand India SDM Govt PG Coll Doiwala Dept Math Dehra Dun 248140 Uttrakhand India PTLMS PG Coll Dept Math Rishikesh 249201 Uttrakhand India Tulas Inst Dept Appl Sci Dehra Dun 248007 Uttrakhand India

Grid computing is employed to unravel massive computational problems by using large numbers of heterogeneous computers connected to the computing network. Job scheduling is an important part of the grid computing environment, which is employed to extend the throughput and reduce the turnaround and reaction time. This paper proposed a new scheduling algorithm called "Feed forward neural network in the grid computing (FFNNGC) system," which is used to solve some real-life problems related to the pattern classification. In the proposed method, we have used a feed-forward algorithm to find the output in the grid computing network, and the network training is done until the system converges to a minimum error solution. The pattern classification problem consists of 13 real-life, and artificial dataset problems, including two class and multiclass problems. Experiments were performed under these real-life problems, and the results indicated that the proposed method is helpful in such types of problems.

关键词： grid computing pattern classification artificial neural network distributed heterogeneous systems feed forward algorithm back propagation algorithm FFNN feed forward neural networks

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Fault detection and classification in electrical power transmission system using artificial neural network

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SPRINGERPLUS 2015年第1期4卷 1页

作者： Jamil, Majid Sharma, Sanjeev Kumar Singh, Rajveer Jamia Millia Islamia Fac Engn Dept Elect Engn New Delhi 110025 India

This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB (R) environment.

关键词： Artificial neural networks Feedforward networks back propagation algorithm Levenberg-Marquardt algorithm

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A Review on Neural Network Models for Wind Speed Prediction

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WIND ENGINEERING 2013年第2期37卷 111-123页

作者： Sheela, K. Gnana Deepa, S. N. Anna Univ Reg Ctr Coimbatore Tamil Nadu India

This paper presents review of neural network models for wind speed prediction during the past 15 years. Wind energy is one of renewable energy system with lowest cost of electricity production. The prediction of wind speed is recognized as a major contribution of wind farm. Wind speed prediction has many applications in Military, Air traffic control and Ship navigation etc. The prediction of wind speed depends on meteorological variables such as pressure, temperature, humidity and rainfall etc. The aim is to identify the importance, advantages and comparison of different neural network models of wind speed prediction. This review is to be useful for researchers working in this field and identify performance of neural network models. It is very helpful to the wind farm owners to understand the current wind prediction model capabilities and give an idea which model will be suitable for predicting the wind speed at their wind farms.

关键词： Wind speed prediction Neural Network Models back propagation algorithm Radial Basis Function Recurrent network

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Distribution locational marginal price computation based on active power loss and emissions in active distribution system using nucleolus theory

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EVOLUTIONARY INTELLIGENCE 2024年第1期17卷 249-265页

作者： Veeramsetty, Venkataramana SR Engn Coll Ctr AI & Deep Learning Dept Elect & Elect Engn Warangal Telangana India

In this study, a Nucleolus Theory (NT) based iterative method has been presented to compute Distribution Locational Marginal Price (DLMP) at buses where embedded generator (EG) units were installed in the distribution network. The NT-based iterative method provides financial incentives to EG owners as per their contribution to loss reduction and emission reduction for specified loading conditions. In this study, DLMP values depend on the decision-maker preference among loss reduction, emission reduction, and distribution company additional benefit. The main objective of the proposed method is to optimize the active power loss and emissions based on DISCO decision-maker priority subjected to EG capacity. DLMP value for each EG unit is a variable that has to compute based on DISCO decision-maker priority. This proposed NT based iterative method has been implemented on Taiwan power company Distribution System (TPCDS) consisting of 84 buses with 15 EG units, and 201 bus radial distribution networks with 15 EG units under MATLAB environment and computed DLMP values based on the contribution of EG units on loss and emission reduction. It was inferred from the results that the proposed method enables EG owners to receive more financial benefit and also enables DISCO decision-makers can operate the network with more optimal in terms of loss and emissions.

关键词： Embedded generator Distribution locational marginal price Loss reduction Emission reduction Nucleolus theory back propagation algorithm Artificial neural network Distribution company

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Material selection processes and exchange of material information in concurrent engineering environment using neural network and web technology

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2014年第4期8卷 235-242页

作者： Balakrishna, A. Rao, D. Nageswara Madhu, T. Rao, V. Durga Prasad SRKR Engn Coll Mech Dept Bhimavaram 534204 India Andhra Univ Dept Mech Engn Visakhapatnam India Swarnandhra Inst Engn & Technol Narsapur India

Selection of the proper materials for a structural component is critical in engineering design. Existing design procedures may currently be sufficient, especially where experience exists, but fierce industrial competition is spurring the search for improved methods and tools. The main drivers are quality, life-cycle cost, time-to-market and material selection. Improved design efficiency and accuracy may depend on specific material selection. This paper discusses the one of the Concurrent Engineering integration strategies i.e., "selection of material" using neural networks that can be adapted for successful integration within industry and beyond using XML and web services. The introduction of computerized design and analysis systems offered the promise of selecting the right material for an optimized design early in the product development cycle. This requires consistent and high-quality material information throughout the design to manufacturing process.

关键词： Material properties Concurrent engineering XML back propagation algorithm

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Numerical Solution of Bratu's Problem Using Multilayer Perceptron Neural Network Method

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NATIONAL ACADEMY SCIENCE LETTERS-INDIA 2015年第5期38卷 425-428页

作者： Kumar, Manoj Yadav, Neha Motilal Nehru Natl Inst Technol Dept Math Allahabad 211004 Uttar Pradesh India ITM Univ Dept Appl Sci Gurgaon 122017 Haryana India

In this article, an artificial neural network (ANN) method is presented to obtain the closed analytic form of the one dimensional Bratu type equations, which are widely applicable in fuel ignition of the combustion theory and heat transfer. Our goal is to provide optimal solution of Bratu type equations with reduced calculus effort using ANN method in comparison to the other existing methods. Various test cases have been simulated using proposed neural network model and the accuracy has been substantiated by considering a large number of simulation data for each model with enough independent runs. Numerical results show that this method has potentiality to become an efficient approach for solving Bratu's problems with less computing time and memory space.

关键词： Artificial neural network back propagation algorithm Bratu's equations Levenberg-Marquardt algorithm Conjugate gradient algorithm

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Neural network identification for underwater vehicle motion control system based on hybrid learning algorithm

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High Technology Letters 2012年第3期18卷 243-247页

作者： Sun Yushan Wang Jianguo Wan Lei Hu Yunyan Jiang Chunmeng State Key Laboratory of Autonomous Underwater Vehicle Harbin Engineering University Harbin 150001 P. R. China China Ship Development and Design Center Wuhan 430064 P. R. China Humanities Department City College of Wuhan University of Science and Technology Wuhan 430083 P. R. China

Based on the structure of Elman and Jordan neural networks, a new dynamic neural network is constructed. The network can remember the past state of the hidden layer and adjust the effect of the past signal to the current value in real-time. And in order to enhance the signal processing capabilities, the feedback of output layer nodes is increased. A hybrid learning algorithm based on genetic algorithm （GA） and error back propagation algorithm （BP） is used to adjust the weight values of the network, which can accelerate the rate of convergence and avoid getting into local optimum. Finally, the improved neural network is utilized to identify underwater vehicle （UV）＇ s hydrodynamic model, and the simulation results show that the neural network based on hybrid learning algorithm can improve the learning rate of convergence and identification nrecision.

关键词： underwater vehicle (UV) system identification neural network genetic algo-rithm (GA) back propagation algorithm

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