Early Model of Traffic Sign Reminder Based on Neural Network

Rahmani, Budi; Supriyadi, Supriyadi

doi:10.12928/telkomnika.v10i4.864

Cited by 4 publications

(3 citation statements)

References 5 publications

(6 reference statements)

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“…Let y i be the measured value of time i or the targeted minimum error on a neural network, f i is the predicted value at time i obtained from a particular model M , and n be the number of sample data. Then, MSE is given by [26]:…”

Section: F Prediction Accuracymentioning

confidence: 99%

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Sugiartawan¹,

Pulungan²,

Sari³

2017

ijacsa

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Abstract-Data originating from some specific fields, for instance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.

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Section: F Prediction Accuracymentioning

confidence: 99%

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Sugiartawan¹,

Pulungan²,

Sari³

2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…An iterative demosaicing technique is essential to derive local knowledge and achieve accurate estimation [18,19]. In recent times the adoption of neural networks in image processing [20,21] to derive knowledge is a motivating factor for the authors of this paper. A neural network based demosaicing technique proposed in [22] bears the closest similarity to the work proposed here.…”

Section: Introductionmentioning

confidence: 99%

Demosaicing of Color Images by Accurate Estimation of Luminance

Prakash¹,

Prasad

Prasad³

2016

TELKOMNIKA

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“…If the selection of transmission line corridor becomes more difficult and the tortuous path coefficient increases, it will cause increases of strain, corner towers and material consumptions, as a result, the body (1) BP neural network BP neural network is a multilayer feed-forward network trained by error backpropagation algorithm.It's widely used and has a strong generalization ability and fault tolerance. BP neural network can learn and store input -output relationship mapping.It's learningrule is the steepest descent method, and through the back-propagation, constantly adjusting the weights and thresholds of the network, so that the squared error reaches the minimum [11]- [12].…”

Section: Introductionmentioning

confidence: 99%

Cost Forecasting Model of Transmission Project based on the PSO-BP Method

Niu

et al. 2014

TELKOMNIKA

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Keywords: PSO, BP neural network, 110kV transmission line, cost forecasting IntroductionTo meet the social demand for electricity, the power grid around has been constructed much faster.The infrastructure investment of the State Grid Corporation is maintained an annual growth rate of over 10%.A reasonable determinationto the cost of construction projects is importantto improve the returns of the power grid investment. At present, it's mainly through the budget quota shall to estimate accurately the project cost [1], but this method has been increasingly unable to meet the requirements of economic development.In the context of not completely collecting the amount of information, it's hard to predict the project cost quickly and efficiently. Therefore, the introduction of advanced cost forecasting methods and the improvementof cost prediction accuracy have important significance.Many scholars and experts launched a studyin the field of power engineering cost, but mainly concentrated in factors affecting cost, cost control and management and other aspects, relatively fewer studies on the cost forecasting model. Literature [2] usesthe fuzzy math theory, and estimate the cost of the project to be builtthrough calculatingthe close degree between the completed projects and the projects to be built; literature [3] adopts a linear regression model to predict the cost; literature [4] holds a regression analysis on the key impactive factors on the cost, using multiple linear regression and factor adjustment to establish a comprehensive cost forecasting model for the transmission project; literature [5] uses the GM(1,1) model to establish two principles calculation models, which was used to compilethe estimates of the power engineering projects.In the application of artificial neural network, literature [6] usesthe cost data of historical power engineering projects for ANN training, and adopts the new ANN after training to the cost forecasting of new power projects; literature [7] proposed an approach based on the combining method of gray relational analysis and the neural network; literature [8] proposed a cost forecasting methodbased on BP neural network of the transmission line projects.However, the BP neural network algorithm exists problems ofbeing sensitive to the initial weights, easy to fall into local minimum and slow convergence [9], so we introduce the PSOalgorithmbased onideas of global stochastic optimization, and adopt the PSO-BP algorithm tothe transmission line cost forecasting.

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Early Model of Traffic Sign Reminder Based on Neural Network

Cited by 4 publications

References 5 publications

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Demosaicing of Color Images by Accurate Estimation of Luminance

Cost Forecasting Model of Transmission Project based on the PSO-BP Method

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