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1 – 10 of over 1000S. Ghanemi and Ben Ali Y. Mohamed
Combining the parallel and neural paradigms seems, at first glance, to be a natural process, since it is a methodology derived from the part played by the biological and…
Abstract
Combining the parallel and neural paradigms seems, at first glance, to be a natural process, since it is a methodology derived from the part played by the biological and mathematical behavior of a neuron. It is proposed that any neural algorithm is inherently a parallel application. The structure of a neural algorithm and the function of a neuron suggest the choice of the systolic approach. However, interest should be restricted only to those well‐known neural models such as the Hopfield and back‐propagation neural networks. It is also shown that the systolic approach is best suited to the parallelization of the patterns training phase of the neural algorithms in terms of mapping the two structures (systolic and neural networks).
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In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of…
Abstract
Purpose
In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.
Design/methodology/approach
In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.
Findings
In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.
Originality/value
In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.
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Anish Pandey and Dayal R. Parhi
This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward…
Abstract
Purpose
This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment.
Design/methodology/approach
Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment.
Findings
Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments.
Originality/value
The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.
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Hassan Al Nageim, Ravindra Nagar and Paulo J.G. Lisboa
To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.
Abstract
Purpose
To investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings.
Design/methodology/approach
Database of 234 design examples has been developed using commercially available detailed design software. These examples represent building up to 20 storeys. Feed forward back‐propagation neural network is trained on these examples. The results obtained from the artificial neural network are evaluated by re‐substitution, hold‐out and ten‐fold cross‐validation techniques.
Findings
Results indicate that artificial neural network would give a performance of 97.91 percent (ten‐fold cross‐validation). The performance of this system is benchmarked by developing a binary logistic regression model from the same data. Performance of the two models has been compared using McNemar's test and receiver operation characteristics curves. Artificial neural network shows a better performance. The difference is found to be statically significant.
Research limitations/implications
The developed model is applicable only to steel building up to 20 storeys. The feasibility of using artificial neural networks for conceptual design of bracings systems for tall steel buildings more than 20 storeys has not been investigated.
Practical implications
Implementation of the broad methodology outlined for the use of neural networks can be accomplished by conducting short training courses. This will provide personnel with flexibility in addressing buildings‐specifics bracing conditions and limitations.
Originality/value
In tall building design a lot of progress has been made in the development of software tools for numerical intensive tasks of analysis, design and optimization, however, professional software tools are not available to help the designer to choose an optimum building configuration at the conceptual design stage. The presented research provides a methodology to investigate the feasibility of using artificial neural networks for conceptual design of bracings systems for tall buildings. It is found that this approach for the selection of bracings in tall buildings is a better and cost effective option compared with database generated on the basis of expert opinion. It also correctly classifies and recommends the type of trussed bracing system.
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Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select…
Abstract
Purpose
Traditional machining centre selection methods may not guarantee a cost effective solution. Properly trained back‐propagation artificial neural network (BPANN) tend to select reasonable machining centres when presented with machining parameters that they have never seen before. The aim of this paper is to demonstrate the applicability of artificial neural networks (ANNs) to machine centre selection problems.
Design/methodology/approach
A three‐layer feedforward back‐propagation supervised training approach is selected to address the machining centre selection problem and demonstrated its potential through an example. This is intended to help readers understand implications on manufacturing system design and future research.
Findings
Very limited studies attempted the machining centre selection problem. Feedforward ANN approach has been applied to a wide variety of manufacturing problems. Neural networks have training capability to solve problems that are difficult for conventional computers or human beings. The developed BPANN model has potential to solve the machine centre selection problem with notable consistency and reasonable accuracy.
Practical implications
The BPANN model is an innovative approach fundamentally based on artificial intelligence, which is not directly visible to the user, but is able to solve through a simpler and supervised feedforward back‐propagation training process. The model consists of an input layer, a hidden layer and an output layer. The 18 neurons fixed in the input layer are same as the set of machining centre parameters which are taken directly from the machine tool manufacturer's catalogues. Evidently the proposed three‐layer ANN model has the capability of solving the machine centre selection problem with three hidden neurons for threshold level of 0.9, noise level of 0.05 and tolerance of 0.01.
Originality/value
The work size, weight, travel range, spindle speed range, horse power, feed, accuracy, tool magazine and price are used as machining centre selection parameters. Machining centres' information in the form of 24 patterns along with the desired machining centres' were used to train and test the network.
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Michiel C. van Wezel and Walter R.J. Baets
Market response modelling is well covered in the marketingliterature. However, much less research has been undertaken in the useof neural networks for market response modelling…
Abstract
Market response modelling is well covered in the marketing literature. However, much less research has been undertaken in the use of neural networks for market response modelling. Describes experiments to fit neural networks to the consumer goods market. Compares the neural network approach with several other possible models. Focuses on the out‐of‐sample performance of the models. Describes a method for adjusting the neural network architecture which leads to better performance on out‐of‐sample data.
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Jinsong Tu, Yuanzhen Liu, Ming Zhou and Ruixia Li
This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.
Abstract
Purpose
This paper aims to predict the 28-day compressive strength of recycled thermal insulation concrete more accurately.
Design/methodology/approach
The initial weights and thresholds of BP neural network are improved by genetic algorithm on MATLAB 2014 a platform.
Findings
Genetic algorithm–back propagation (GA-BP) neural network is more stable. The generalization performance of the complex is better.
Originality/value
The GA-BP neural network based on the training sample data can better realize the strength prediction of recycled aggregate thermal insulation concrete and reduce the complex orthogonal experimental process. GA-BP neural network is more stable. The generalization performance of the complex is better.
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Joseph Awoamim Yacim and Douw Gert Brand Boshoff
The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the…
Abstract
Purpose
The paper aims to investigate the application of particle swarm optimisation and back propagation in weights optimisation and training of artificial neural networks within the mass appraisal industry and to compare the performance with standalone back propagation, genetic algorithm with back propagation and regression models.
Design/methodology/approach
The study utilised linear regression modelling before the semi-log and log-log models with a sample of 3,242 single-family dwellings. This was followed by the hybrid systems in the selection of optimal attribute weights and training of the artificial neural networks. Also, the standalone back propagation algorithm was used for the network training, and finally, the performance of each model was evaluated using accuracy test statistics.
Findings
The study found that combining particle swarm optimisation with back propagation in global and local search for attribute weights enhances the predictive accuracy of artificial neural networks. This also enhances transparency of the process, because it shows relative importance of attributes.
Research limitations/implications
A robust assessment of the models’ predictive accuracy was inhibited by fewer accuracy test statistics found in the software. The research demonstrates the efficacy of combining two models in the assessment of property values.
Originality/value
This work demonstrated the practicability of combining particle swarm optimisation with back propagation algorithms in finding optimal weights and training of the artificial neural networks within the mass appraisal environment.
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Xiaohong Lu, Yongquan Wang, Jie Li, Yang Zhou, Zongjin Ren and Steven Y. Liang
The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive…
Abstract
Purpose
The purpose of this paper is to solve the problem that the analytic solution model of spatial three-dimensional coordinate measuring system based on dual-position sensitive detector (PSD) is complex and its precision is not high.
Design/methodology/approach
A new three-dimensional coordinate measurement algorithm by optimizing back propagation (BP) neural network based on genetic algorithm (GA) is proposed. The mapping relation between three-dimensional coordinates of space points in the world coordinate system and light spot coordinates formed on dual-PSD has been built and applied to the prediction of three-dimensional coordinates of space points.
Findings
The average measurement error of three-dimensional coordinates of space points at three-dimensional coordinate measuring system based on dual-PSD based on GA-BP neural network is relatively small. This method does not require considering the lens distortion and the non-linearity of PSD. It has simple structure and high precision and is suitable for three-dimensional coordinate measurement of space points.
Originality/value
A new three-dimensional coordinate measurement algorithm by optimizing BP neural network based on GA is proposed to predict three-dimensional coordinates of space points formed on three-dimensional coordinate measuring system based on dual-PSD.
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Investigates the possibility of applying artificial intelligence tosolve practical auditing problems faced by the public sector, namely thetax auditor of the Internal Revenue…
Abstract
Investigates the possibility of applying artificial intelligence to solve practical auditing problems faced by the public sector, namely the tax auditor of the Internal Revenue Services, when targeting firms for further investigation. Suggests that organizations which incorporate an operational artificial neural network system will raise their performance greatly. Proposes that the neural network will overcome problems faced by a direct knowledge acquisition method in building an expert system to preserve the expertise of senior auditors by the IRS in Taiwan. Provides an explanation of the neural network theory with regard to multi‐ and single‐layered neural networks. Statistics reveal the neural network performs favourably, and that three‐layer networks perform better than two‐layer neural networks. The results strongly suggest that neural networks can be used to identify firms requiring further auditing investigation, and also suggest future implications for intelligent auditing machines.
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