A Jordan Pi-Sigma Neural Network for Temperature Forecasting in Batu Pahat Region

Husaini, Noor Aida; Ghazali, Rozaida; Ismail, Lokman Hakim; Herawan, Tutut

doi:10.1007/978-3-319-07692-8_2

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The backpropagation neural network training algorithm consists of two steps, namely forward propagation and backward propagation, steps of both forward and backward propagations are conducted on a network for each given pattern during the training network [14][15][16][17]. In a given network a pair of patterns comprising the desired input and output patterns, when a pattern is assigned to the network, so the weights are changed to minimize the difference in the network output and the desired output pattern [18][19][20][21].…”

Section: Classification Design With Backpropagation Neural Networkmentioning

confidence: 99%

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

Wibowo¹,

Harjoko²,

Mustafidah³

et al. 2018

IJAST

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The quality identification process using a visual human eyes has its limitations, it requires more power to sort out. Furthermore, it has different levels of human perception in identifying the quality. This research develops digital image processing programs and artificial neural network for the quality classification of papaya Calina IPB-9 into three quality classes: Super, A, and B classes. Shape feature is extracted consistsing of compactness and roundness. Texture feature which is extracted consisting of the value of energy, entropy, contras, homogeneity, inverse difference moment, variance, and dissimilarity obtained by GLCM, another texture feature sought a feature LBP. Color feature is extracted consisting of the mean red, greeen, blue, hue, saturation, and value. These features serve as input to the training backpropagation neural network. The recognition process results shows the features of energy and entropy can distinguish quality classes of the papaya with the best accuracy rate that is equal to 87%.

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Section: Classification Design With Backpropagation Neural Networkmentioning

confidence: 99%

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

Wibowo¹,

Harjoko²,

Mustafidah³

et al. 2018

IJAST

View full text Add to dashboard Cite

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“…The structure of PSNN is highly regular due the fact that the summing units can be added incrementally until a specified goal is achieved. Despite the fact that PSNN is not a universal approximator [27], it demonstrated competent ability to deal with many problems such as classification [28], time series forecasting [19], image coding [29] and visual cryptography [30].…”

Section: Pi-sigma Neural Network (Psnn)mentioning

confidence: 99%

Chaotic Time Series Forecasting Using Higher Order Neural Networks

Waheeb

Ghazali

2016

International Journal on Advanced Science, Engineering and Information Technology

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This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models.

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“…Numerous computational intelligence (CI) techniques have emerged motivated for solving many real world problems by real biological systems, namely, artificial neural networks (NNs) [13][14][15][16][17][18][19][20][21][22] , evolutional computation, simulated annealing and swarm intelligence, which were enthused by biological nervous systems, natural selection, the principle of thermodynamics and insect behavior, respectively. Despite the limitations associated with each of these mentioned techniques, they are robust and have been applied in solving real life problems in the areas of science, technology, business and commerce including the works of [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network approach for detecting skin cancer

et al. 2019

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This study aims to present diagnose of melanoma skin cancer at an early stage. It applies feature extraction method of the first order for feature extraction based on texture in order to get high degree of accuracy with method of classification using artificial neural network (ANN). The method used is training and testing phases with classification of Multilayer Perceptron (MLP) neural network. The results showed that the accuracy of test image with 4 sets of training for image not suspected of melanoma and melanoma with the lowest accuracy of 80% and the highest accuracy of 88.88%, respectively. The 4 sets of training used consisted of 23 images. Of the 23 images used as a training consisted of 6 as not suspected of melanoma images and 17 as suspected melanoma images.

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A Jordan Pi-Sigma Neural Network for Temperature Forecasting in Batu Pahat Region

Cited by 7 publications

References 16 publications

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

The Classification of Papaya using Digital Image Processing and Artificial Neural Networks

Chaotic Time Series Forecasting Using Higher Order Neural Networks

An artificial neural network approach for detecting skin cancer

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