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Study on the Construction of English ICAI Course Based on BP neural network Algorithm

Journal of Physics: conference Series 2021年第2期1992卷

作者： Yin Wei Jilin Engineering Normal University Jilin China

Artificial neural network is also referred to as neural network or connection model, it's a model animal neural network, the mathematical model of distributed and information processing algorithm. Because of its good abstract classification, Has been used in all aspects of life. And in recent years, the traditional English teaching mode in colleges and universities has been gradually replaced by CAI new teaching methods, much progress has been made, Increasing resources to open schools and curricula, effectively promote the sharing of quality teaching resources, it promotes the improvement of students' English listening, speaking, reading, writing and translation. But the general problems of English CAI teaching have gradually emerged, for example, curriculum design does not pay enough attention to individual differences, online teaching lacks interaction between teachers and students, lack of effective supervision and assessment of learning results. At present, the student-centered teaching concept based on the class-flipping classroom and the 'online and offline' mixed teaching model is becoming popular, which has had a great impact on the traditional foreign language teaching model. Designing English intelligent computer-assisted teaching (intelligent computer-assisted teaching) can intelligently meet the individual needs of students according to the learning effect, realize the interaction between teachers and students through human-computer interaction, and facilitate the management and evaluation of the teaching process by managers. This is referred to as ICAI. It is an urgent problem in English teaching, especially in the teaching of college English public courses. This paper designs an English ICAI language curriculum system based on BP neural network algorithm and SSH architecture.

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network Based on Improved Genetic Algorithms

Network Based on Improved Genetic Algorithms

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2019 7th International conference on Machinery,Materials and Computing Technology(ICMMCT 2019)

作者： Qi Anzhi Liaoning Jianzhu Vocational College

BP neural network is a kind of non-linear system which simulates brain information processing *** has strong distributed information storage,parallel processing and adaptive learning *** is a multi-layer feed forward network trained by error back propagation *** it with genetic algorithm,a learning and training approach with good global optimization search and local time-frequency characteristics can be *** this paper,the crossover rate of genetic algorithm is improved,and the efficiency of the algorithm is *** the same time,in order to solve the problem that the initial population is far away from the optimal individual,the convergence speed is slow and it is easy to fall into the local *** information gain of the feature is used as the degree of discrimination of each feature to the *** this basis,it is used to optimize the BP neural network input layer neuron screening *** experimental results show that the efficiency of the traditional method is significantly improved.

关键词： Improved genetic algorithm BP neural network Neuron

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Classifying Alcoholics and Control Patients Using Deep Learning and Peak Visualization Method 2019

Classifying Alcoholics and Control Patients Using Deep Learn...

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Proceedings of the 3rd International conference on Vision, Image and Signal processing

作者： Asim Fayyaz Muaz Maqbool Mehreen Saeed FAST NUCES Lahore Pakistan

ISBN: (纸本)9781450376259

A lot of advancements have been made in the field of Brain Computer Interfaces (BCI) using machine and deep learning. This paper presents a novel preprocessing technique to process Electroencephalography (EEG) signals in time domain. The proposed methodology, (Peak Visualization Method) (PVM) is based on selecting peaks with distinctive width and height range in order to perform better classification. PVM uses multiple machine learning techniques such as Random Forest, Logistic Regression and Support Vector Machine (SVM), Naive Bayes in order to find the most discriminating ranges. Moreover, selected range peaks are further used to compute features like indices of peaks, prominence of peak, contour heights, relative maxima, relative minima, local maxima and local minima. The extracted features were used as training and test data for a competitive 5-fold cross-validated analysis with Long Short Term Memory (LSTM) network. A publicly available EEG dataset for alcoholic and non-alcoholic classification was used to compare the proposed technique with state of the art EEG-NET deep learning model. In order to visualize the generalized performance of proposed system we use award winning dimensionality reduction technique t-distributed Stochastic Neighbor Embedding (t-SNE) on the features extracted by EEGLSTM and show how our model's activations are classified in alcoholic and non-alcoholic categories. The reduced features are visualized into two *** extracted using PVM gives an average accuracy of 90% on 5 folds beating the current state of the art EEG-NET, which manages to achieve an average accuracy of 88% on this dataset.

关键词： EEG classification preprocessing technique signal processing TSNE visualization deep neural networks

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Neurodynamics-Based distributed Receding Horizon Trajectory Generation for Autonomous Surface Vehicles 25th

Neurodynamics-Based Distributed Receding Horizon Trajectory ...

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25th International conference on neural Information processing (ICONIP)

作者： Wang, Jiasen Wang, Jun City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Peoples R China

ISBN: (纸本)9783030042394;9783030042387

This paper presents a neurodynamics-based distributed algorithm for trajectory generation for a group of autonomous surface vehicles (ASVs). By means of convexification, the trajectory generation problem is formulated as a distributed optimization problem with affine constraints and quadratic objectives. Neurodynamic approach and receding horizon mechanism are used for solving the distributed optimization problem. Simulation results on generating trajectories for four fully-actuated and under-actuated ASVs are reported to substantiate the efficacy of the algorithm.

关键词： Autonomous surface vehicles Recurrent neural networks Receding horizon planning

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N2SkyC-User Friendly and Efficient neural network Simulation Fostering Cloud Containers 25th

N2SkyC-User Friendly and Efficient Neural Network Simulation...

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25th International conference on neural Information processing (ICONIP)

作者： Adamenko, Aliaksandr Fedorenko, Andrii Schikuta, Erich Univ Vienna Fac Comp Sci Wahringerstr 29 A-1090 Vienna Austria

ISBN: (纸本)9783030042240;9783030042233

Y Sky computing is a new computing paradigm leveraging resources of multiple Cloud providers to create a large scale distributed infrastructure. N2Sky is a research initiative promising a framework for the utilization of neural networks as services across many Clouds. This involves a number of challenges ranging from the provision, discovery and utilization of services to the management, monitoring, metering and accounting of the infrastructure. Cloud Container technology offers fast deployment, good portability, and high resource efficiency to run large-scale and distributed systems. In recent years, container-based virtualization for applications has gained immense popularity. This paper presents the new N2SkyC system, a framework for the utilization of neural networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.

关键词： Problem solving environment neural networks Cloud computing Containers Microservices

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Optimal Algorithms for Non-Smooth distributed Optimization in networks 32

Optimal Algorithms for Non-Smooth Distributed Optimization i...

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32nd conference on neural Information processing Systems (NIPS)

作者： Scaman, Kevin Bach, Francis Bubeck, Sebastien Lee, Yin Tat Massoulie, Laurent Huawei Noahs Ark Lab Paris France PSL Res Univ Ecole Normale Super INRIA Paris France Microsoft Res Redmond WA USA Univ Washington Seattle WA 98195 USA MSR INRIA Joint Ctr Paris France

In this work, we consider the distributed optimization of non-smooth convex functions using a network of computing units. We investigate this problem under two regularity assumptions: (1) the Lipschitz continuity of the global objective function, and (2) the Lipschitz continuity of local individual functions. Under the local regularity assumption, we provide the first optimal first-order decentralized algorithm called multi-step primal-dual (MSPD) and its corresponding optimal convergence rate. A notable aspect of this result is that, for non-smooth functions, while the dominant term of the error is in O (1/root t), the structure of the communication network only impacts a second-order term in O (1/root t), where t is time. In other words, the error due to limits in communication resources decreases at a fast rate even in the case of non-strongly-convex objective functions. Under the global regularity assumption, we provide a simple yet efficient algorithm called distributed randomized smoothing (DRS) based on a local smoothing of the objective function, and show that DRS is within a d(1/4) multiplicative factor of the optimal convergence rate, where d is the underlying dimension.

关键词： Functions

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A stock forecasting method based on combination of SDAE and BP 6

A stock forecasting method based on combination of SDAE and ...

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International conference on Orange Technologies (ICOT)

作者： Li, Zheng Dang, Xin Tianjin Polytech Univ Sch Comp Sci & Software Engn Tianjin Peoples R China

ISBN: (纸本)9781538673195

The neural network, as a non-linear system with large-scale parallel distributed processing, has been widely used in the field of prediction. This paper proposes a stock forecasting method based on the combination of Stacked Denoising AutoEncoders (SDAE) and Back Propagation neural network (BPNN). Using more than 4100 historical data of Sinopec Group from 2001/8/8 to 2018/8/16 for Seventeen years, then process the data and select six features of them for training and prediction. The experiment first uses SDAE for training in the pre-training stage to obtain the weights. Then the trained weights of SDAE are assigned to the Back Propagation neural network (BPNN) for fine-tuning to optimize the entire network structure. Then optimize the network parameters for further step and chose the best optimal hyperparameter combination. Finally, the prediction is performed on the test set, the Mean Absolute Error (MAE), Error Variance (EV), and Absolute Maximum Error (AME) between the predicted value and the actual value are used as evaluation performance evaluation criteria. The results show that this neural network model can achieve high precision, and provides an effective method for predicting the stock market artificial neural network with many influencing factors and unclear mechanism.

关键词： neural network Stock Prediction Stacked Denoising AutoEncoder (SDAE) Back Propagation neural network (BPNN)

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Research on Path Selection for Space network Based on Fuzzy neural network

Research on Path Selection for Space Network Based on Fuzzy ...

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International conference on Wireless Communications and Signal processing (WCSP)

作者： Yueyue Zhang Yunchi Shi Yihuan Pang Fei Shen Feng Yan Lianfeng Shen School of Information Science and Engineering Southeast University Nanjing Jiangsu P. R. China Shanghai Aerospace Electronic Technology Institute Shanghai China Key Lab for Wireless Sensor Network and Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences China

With the development of space communication technology, space network is gradually enhanced by distributed spacecraft. This novel architecture of space network brings new challenges on self-organization and self-adaptation to inter spacecraft communication and packet routing From the perspective of delay tolerant network. In This paper, we propose an efficient and flexible routing algorithm by taking consideration of the constraint of the limited payload resource. In order to meet the requirement of complex interactive information exchanged among the spacecraft, a path selection algorithm is designed based on cascade fuzzy neural network (FNN) scheme, which mainly consists of a FNN processing module with four parts, ie. fuzzification, inference, defuzzification and rule database. Therefore, the information concerning the relative movement velocity, distance and storage capacity of each satellite can be formulated as a multidimensional vector to describe the characteristics of the space network. Additionally, the stability of each link could be derived via the first-level FNN, and then the probability of successful transmission measuring each link would be obtained through the second-level FNN. Finally, the performance of the proposed algorithm is analyzed and verified by the simulation.

关键词： Fuzzy neural networks Space vehicles Routing Heuristic algorithms Encoding Convolutional codes Decoding

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Matching Cost Calculation Model Based on Multi-Scale Convolutional neural network

Matching Cost Calculation Model Based on Multi-Scale Convolu...

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IEEE International Symposium on Parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Tengna Wang Lin Ye Xian branch of China telecom co. LTD Xi’an China Shaanxi branch of China telecom co. LTD Xi’an China

The following topics are dealt with: learning (artificial intelligence); feature extraction; convolutional neural nets; Big Data; data mining; social networking (online); pattern clustering; telecommunication computin... 详细信息

ISBN: (纸本)9781728152103

关键词： convolutional neural nets feature extraction image matching stereo image processing Stereo image processing Computational modeling image matching Feature extraction multi-scale neural network Costs

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A Low-Power Convolutional neural network Face Recognition Processor and a CIS Integrated With Always-on Face Detector

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2018年第1期53卷 115-123页

作者： Bong, Kyeongryeol Choi, Sungpill Kim, Changhyeon Han, Donghyeon Yoo, Hoi-Jun Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 305701 South Korea

A Low-power convolutional neural network (CNN)-based face recognition system is proposed for the user authentication in smart devices. The system consists of two chips: an always-on CMOS image sensor (CIS)-based face detector (FD) and a low-power CNN processor. For always-on FD, analog-digital Hybrid Haar-like FD is proposed to improve the energy efficiency of FD by 39%. For low-power CNN processing, the CNN processor with 1024 MAC units and 8192-bit-wide local distributed memory operates at near threshold voltage, 0.46 V with 5-MHz clock frequency. In addition, the separable filter approximation is adopted for the workload reduction of CNN, and transpose-read SRAM using 7T SRAM cell is proposed to reduce the activity factor of the data read operation. Implemented in 65-nm CMOS technology, the 3.30 x 3.36 mm(2) CIS chip and the 4 x 4 mm(2) CNN processor consume 0.62 mW to evaluate one face at 1 fps and achieved 97% accuracy in LFW dataset.

关键词： Always-on system convolutional neural network (CNN) face recognition (FR) functional CMOS image sensor (CIS)

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