Improved generalized neuron model for short-term load forecasting

Chaturvedi, D. K.; Mohan, Maya; Singh, Raghuraj; Kalra, Prem Kumar

doi:10.1007/s00500-003-0303-1

Cited by 29 publications

(4 citation statements)

References 13 publications

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“…ANNs have several drawbacks (e.g., especially for complex problems, long training time, large training data set, problem of local minima), so many variants were developed in the past decades, including models based on generalized neurons, but the errors in some cases turn out to be not acceptable. 34 An interesting alternative to ANNs is the FNs.…”

Section: Functional Network: An Overviewmentioning

confidence: 99%

A Generalized Functional Network for a Classifier-Quantifiers Scheme in a Gas-Sensing System

Gaeta

Loia

Tomasiello

2013

Int. J. Intell. Syst.

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This paper discusses a new computational scheme based on functional networks and applies it to the problem of classification and quantification of gas species in a mixture. A generalized functional network as a new classifier is proposed to improve the potentialities of the standard functional network classifier. Both methodology and learning algorithm are derived. The performance of this new classifier is examined by using experimental applications. A comparative study with the most common classification algorithms is carried out by showing the high‐quality performance of the proposed classifier. The classifier interacts with some quantifiers, again based on functional networks and finite differences. The scheme of the quantifiers was previously proposed for single gas exposure applications and is here extended to the multigas case. Numerical results show that our approach behaves quite satisfactorily.

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Section: Functional Network: An Overviewmentioning

confidence: 99%

A Generalized Functional Network for a Classifier-Quantifiers Scheme in a Gas-Sensing System

Gaeta

Loia

Tomasiello

2013

Int. J. Intell. Syst.

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“…The learning rate at which the ANN is learning can be escalated by taking the optimum values of weights in very stage (Chaturvedi et al 2004). The total numbers of iterations required for conversing the algorithm for pre define error and time taken in entire training depends upon the following factors:

The structure of the neural network

The size of the neural network (number of layers, etc.

…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Fault detection and classification in electrical power transmission system using artificial neural network

2015

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This paper focuses on the detection and classification of the faults on electrical power transmission line using artificial neural networks. The three phase currents and voltages of one end are taken as inputs in the proposed scheme. The feed forward neural network along with back propagation algorithm has been employed for detection and classification of the fault for analysis of each of the three phases involved in the process. A detailed analysis with varying number of hidden layers has been performed to validate the choice of the neural network. The simulation results concluded that the present method based on the neural network is efficient in detecting and classifying the faults on transmission lines with satisfactory performances. The different faults are simulated with different parameters to check the versatility of the method. The proposed method can be extended to the Distribution network of the Power System. The various simulations and analysis of signals is done in the MATLAB® environment.

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“…d) The fourth model is the same as the third model with a slight difference that ݂ ଵ is Sigmoid and ݂ ଶ is Gaussian [4].…”

Section: Application Of Generalized Neuron Inmentioning

confidence: 99%

Application of generalized neuron in electricity price forecasting

Mirzazad-Barijough

Sahari

2009

2009 IEEE Bucharest PowerTech

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With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of training data and neurons either for complex function approximation and data fitting or classification and pattern recognition. As a result, the network topology has a significant impact on the network computational time and ability to learn and also to generate unseen data from training data. To overcome these problems, a new structure using generalized neurons (GN) is adapted in this paper. The proposed structure needs a smaller data set for training. So this property of GN can be very useful for price forecasting. The data such as historical prices are not available enough for most markets. The significance, viability and efficiency of the proposed approach, in electricity price forecasting, are shown using Ontario market data points and various GN models are compared.

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Improved generalized neuron model for short-term load forecasting

Cited by 29 publications

References 13 publications

A Generalized Functional Network for a Classifier-Quantifiers Scheme in a Gas-Sensing System

A Generalized Functional Network for a Classifier-Quantifiers Scheme in a Gas-Sensing System

Fault detection and classification in electrical power transmission system using artificial neural network

Application of generalized neuron in electricity price forecasting

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