<b>Neural networks: a comprehensive foundation</b> by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7.

Kubát, Miroslav

doi:10.1017/s0269888998214044

Cited by 214 publications

(99 citation statements)

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“…According to Simon [31], neural network can be defined as "a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use". Usually the structure of neural network consists of an input layer with single or multiple neuron (s), one or more hidden layers with single or multiple neurons, and one output layer with single or multiple neurons.…”

Section: Artificial Neural Network (Ann) Methodsmentioning

confidence: 99%

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Gas turbine gas path diagnostics: A review

2016

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Abstract. In this competitive business world one way to increase profitability of a power production unit is to reduce the operation and maintenance expenses. This is possible if the gas turbine availability and reliability is improved using the appropriate maintenance action at the right time. In that case, fault diagnostics is very critical and effective and advanced methods are essential. Gas turbine diagnostics has been studied for the past six decades and several methods are introduced. This paper aims to review and summarise the published literature on gas path diagnostics, giving more emphasis to the recent developments, and identify advantages and limitations of the methods so that beginners in diagnostics can easily be introduced. Towards this end, this paper, identifies various diagnostic methods and point out their pros and cons. Finally, the paper concludes the review along with some recommended future works.

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Section: Artificial Neural Network (Ann) Methodsmentioning

confidence: 99%

“…Figure 2 illustrates the schematics of the topology of artificial neural networks. And the detailed description of different neural network techniques is available in [31]. [9,31].…”

Section: Artificial Neural Network (Ann) Methodsmentioning

confidence: 99%

Gas turbine gas path diagnostics: A review

2016

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“…They are the widely used NNs, particularly in systems and controls. Multilayer NNs have input layer, hidden layers, and output layer (no interconnections between the nodes in same layers), they called a hidden and exist in between two layers, input and also output layer [10], [11]. …”

Section: Categories Of Effort Estimationmentioning

confidence: 99%

Recurrent Neural Network based Prediction of Software Effort

Hussein¹,

Nassar²,

Naser³

2017

IJCA

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The enormous efforts of software systems and unexpected efforts in the late phases of software development in software engineering field led to using methods to estimate software effort at early stages of software preparing phases. Therefore, the question remains how can develop an estimation method to be more accurate and gives a prediction for future software efforts. This paper presents a proposed method for software effort prediction, to enhance software effort estimation phase. The proposed method utilizes feed-forward neural network in recurrent fashion to make a prediction and adapt to handle with varying software types in software engineering. The proposed method (RFFNN) used to enhance the results of ordinary software effort estimation methods, RFFNN gives more efficient results by making a prediction for future software efforts. General TermsSoftware engineering

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“…In order to update the weights, the error between the predicted and actual output values is back propagated via the network. Minimizing of the error the desired and predicted output attempts occurs after the procedure of supervised learning [18]. The architecture of this network contains a hidden layer of neurons with non-linear transfer function and an output layer of neurons with non-linear transfer function and an output layer of neurons with linear transfer functions.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Predicting Financial Time Series Data Using Hybrid Model

Alhnaity

Abbod

2016

Studies in Computational Intelligence

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Prediction of Financial time series is described as one of the most challenging tasks of time series prediction, due to its characteristics and their dynamic nature. Support vector regression (SVR), Support vector machine (SVM) and back propagation neural network (BPNN) are the most popular data mining techniques in prediction financial time series. In this paper a hybrid combination model is introduced to combine the three models and to be most beneficial of them all. Quantization factor is used in this paper for the first time to improve the single SVM and SVR prediction output. And also genetic algorithm (GA) used to determine the weights of the proposed model. FTSE100, S&P 500 and Nikkei 225 daily index closing prices are used to evaluate the proposed model performance. The proposed hybrid model numerical results shows the outperform result over all other single model, traditional simple average combiner and the traditional time series model Autoregressive (AR).

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Neural networks: a comprehensive foundation by Simon Haykin, Macmillan, 1994, ISBN 0-02-352781-7.

Cited by 214 publications

References 0 publications

Gas turbine gas path diagnostics: A review

Gas turbine gas path diagnostics: A review

Recurrent Neural Network based Prediction of Software Effort

Predicting Financial Time Series Data Using Hybrid Model

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