scholarly journals Tensorized Neural Network for Multi-Aspect Rating-Based Recommendation

2019 ◽  
Vol 8 (2S11) ◽  
pp. 547-551
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Activation Function ◽  
Research Area ◽  
Experimental Result ◽  
Computational Time ◽  
Local Optimum ◽  
Tensor Factorization ◽  
Value Decomposition

Generating personalized recommendations is one of the most crucial aspects in Recommender System research area. Most of the researches only focus on the accuracy of recommendation using collaborative filtering that relies on a single overall rating that represents the overall preferences. However, the user may have a different emphasis on different specific aspects that affect the users’ final rating decisions. Therefore, we present a neural network model that utilize multi-aspects ratings using Tensor Factorization to improve the accuracy of personalization, as well as optimizing the dynamic weights of the aspect. To measure the estimated weights for the aspects, we employ the Higher Order Singular Value Decomposition (HOSVD) technique called CANDECOMP/PARAFAC (CP) decomposition that allows for multi-faceted data processing. We then develop the Neural Network with backpropagation error to train the model with different parameter settings and limited computational time. We also use a non-linear activation function in each hidden layer in various settings. The experimental result measured using MAE shows that the proposed model has significantly outperformed the baseline approach in terms of the prediction accuracy. Based on the observation, the performance of rating prediction has been improved by employing the Tensorized Neural Network model and can overcome the problem of local optimum convergence for multi-aspect rating recommendation.

Study on the Levenberg-Marquardt Neural Network Model for Rock Rheology

2011 ◽  
Vol 71-78 ◽  
pp. 4103-4108
Author(s):  
Yu Zhou Jiang ◽  
Rui Hong Wang ◽  
Jie Bing Zhu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Testing Machine ◽  
Experimental Result ◽  
Left Bank ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Rheological Experiments ◽  
Rock Rheology

Rheological experiments were carried out for sandstone and marble specimens from left bank high slope of Jingping First Stage Hydropower Project by using the rock servo-controlling rheology testing machine. Typical triaxial rheological curves under step loading and temperature curves in the process of rheological experiment were gained. BP neural network is improved by Levenberg-Marquardt algorithm. Improved neural network model for rock rheology is established in accordance with the rheology experimental results of rock specimen. The improved neural network model was used to forecast rock rheological experimental curves, and the result shows that the forecasted rock rheology curves are closely accorded with the experimental result. The improved neural network model takes into account the influence of loading history and temperature difference on the rock rheological deformation, and the forecasted result can reflect better the rheology deformation behavior of rock material.

A NEURAL NETWORK MODEL WITH FEATURE SELECTION FOR KOREAN SPEECH ACT CLASSIFICATION

2004 ◽  
Vol 14 (06) ◽  
pp. 407-414 ◽  
Author(s):  
KYUNGSUN KIM ◽  
HARKSOO KIM ◽  
JUNGYUN SEO
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Speech Act ◽  
Feature Selection Method ◽  
Selection Method ◽  
Morphological Features ◽  
Experimental Result ◽  
Real Field

A speech act is a linguistic action intended by a speaker. Speech act classification is an essential part of a dialogue understanding system because the speech act of an utterance is closely tied with the user's intention in the utterance. We propose a neural network model for Korean speech act classification. In addition, we propose a method that extracts morphological features from surface utterances and selects effective ones among the morphological features. Using the feature selection method, the proposed neural network can partially increase precision and decrease training time. In the experiment, the proposed neural network showed better results than other models using comparatively high-level linguistic features. Based on the experimental result, we believe that the proposed neural network model is suitable for real field applications because it is easy to expand the neural network model into other domains. Moreover, we found that neural networks can be useful in speech act classification if we can convert surface sentences into vectors with fixed dimensions by using an effective feature selection method.

scholarly journals General Recurrent Neural Network for Solving Generalized Linear Matrix Equation

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhan Li ◽  
Hong Cheng ◽  
Hongliang Guo
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Matrix Equation ◽  
Activation Function ◽  
Linear Matrix ◽  
Activation Functions ◽  
State Matrix ◽  
Linear Matrix Equation

This brief proposes a general framework of the nonlinear recurrent neural network for solving online the generalized linear matrix equation (GLME) with global convergence property. If the linear activation function is utilized, the neural state matrix of the nonlinear recurrent neural network can globally and exponentially converge to the unique theoretical solution of GLME. Additionally, as compared with the case of using the linear activation function, two specific types of nonlinear activation functions are proposed for the general nonlinear recurrent neural network model to achieve superior convergence. Illustrative examples are shown to demonstrate the efficacy of the general nonlinear recurrent neural network model and its superior convergence when activated by the aforementioned nonlinear activation functions.

The Application of Neural Network in the Bionic Control System of an Artificial Heart Based on Neuroshell

2013 ◽  
Vol 709 ◽  
pp. 862-866
Author(s):  
Teng Jing ◽  
Fang Gun Wang ◽  
Kun Xi Qian
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Heart ◽  
Pressure Head ◽  
Activation Function ◽  
Rotating Speed ◽  
The Neural Network ◽  
Blood Pumps

With the development of heart pumps, more and more commercial artificial heart have been applied to clinic use. However, most of the products produce some discomfort to human body, which coudn’t meet the physiological requirements of patients. Therefore, further improvement and enhancement for these products are needed and adopting bionic control using neural network is an effective method to improve heart pumps’ comfort. A neural network model was established in this paper according to the relationship among the pressure head, the motor power and the rotating speed using using Based on neural network software Neuroshell2. Based on the defined appropriate activation function, a lot of data were studied and trained for optimization of the neural network model and determination of weights and deviations . Finally, the bionic control system was built and the experiments of the control system were conducted. The results reveal that the error measured values and the actual values was within 5% and acceptable and that the bionic control system using neural network is proved to improve the the comfort after implantation of blood pumps.

Periodic oscillation for a complex-valued neural network model with discrete and distributed delays

2020 ◽  
Vol 8 (5) ◽  
Author(s):  
Chunhua Feng
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Sufficient Conditions ◽  
Periodic Oscillation ◽  
Activation Function ◽  
Distributed Delays ◽  
Oscillatory Solutions ◽  
Complex Valued ◽  
Theoretical Results

In this paper, a complex-valued neural network model with discrete and distributed delays is investigated under the assumption that the activation function can be separated into its real and imaginary parts. Based on the mathematical analysis method, some sufficient conditions to guarantee the existence of periodic oscillatory solutions are established. Computer simulation is given to illustrate the validity of the theoretical results.

scholarly journals MoView Engine : An Open Source Movie Recommender

2020 ◽  
Vol 32 ◽  
pp. 03008
Author(s):  
Vallari Manavi ◽  
Anjali Diwate ◽  
Priyanka Korade ◽  
Anita Senathi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Human Brain ◽  
Network Model ◽  
Neural Network Model ◽  
Recommendation System ◽  
Activation Function ◽  
End Users ◽  
History Of

Recommendation is an ideology that works as choice-based system for the end users. Users are recommended with their favorite movies based on history of other watched movies or based on the category of the movies. These types of recommendations are becoming popular because of their ability to think and react as human brain. For this purpose, deep learning or artificial intelligence comes into picture. It is the ability to think as a human brain as give the output best suited to the end users liking. This paper focuses on implementing the recommendation system of movies using deep learning with neural network model using the activation function of SoftMax to give an experience to users as friendly recommendation. Moreover, this paper focuses on different scenarios of recommendation like the recommendation based on history, genre of the movie etc.

scholarly journals Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Hao Peng ◽  
Han Wu ◽  
Junwu Wang ◽  
Tayfun Dede
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Water Consumption ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Water Demand ◽  
Average Error ◽  
Local Optimum ◽  
Construction Engineering ◽  
Bp Neural Network Model

The scientific and effective prediction of the water consumption of construction engineering is of great significance to the management of construction costs. To address the large water consumption and high uncertainty of water demand in project construction, a prediction model based on the back propagation (BP) neural network improved by particle swarm optimization (PSO) was proposed in the present work. To reduce the complexity of redundant input variables, this model determined the main influencing factors of water demand by grey relational analysis. The BP neural network optimized by PSO was used to obtain the predicted value of the output interval, which effectively solved the shortcomings of the BP neural network model, including its slow convergence speed and easy to fall into local optimum problems. In addition, the water consumption interval data of the Taiyangchen Project located in Xinyang, Henan Province, China, were simulated. According to the results of the case study, there were four main factors that affected the construction water consumption of the Taiyangchen Project, namely, the intraday amount of pouring concrete, the intraday weather, the number of workers, and the intraday amount of wood used. The predicted data were basically consistent with the actual data, the relative error was less than 5%, and the average error was only 2.66%. However, the errors of the BP neural network model, the BP neural network improved by genetic algorithm, and the pluralistic return were larger. Three conventional error analysis tools in machine learning (the coefficient of determination, the root mean squared error, and the mean absolute error) also highlight the feasibility and advancement of the proposed method.

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