Probabilistic Neural Networks

Masters, Timothy

doi:10.1016/b978-0-08-051433-8.50017-3

Cited by 265 publications

(58 citation statements)

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“…ANN has various network architectures such as multilayer perceptron (MLP) and radial basis function (RBF). In this paper, we test the RBF network for two reasons: first, the main disadvantage of the MLP network is that its local minima are limited and that its astringency is slow [41]; second, the RBF network performs better than the MLP network in terms of approximation capability, classification capacity and learning rate [42]. Broomhead and Lowe [43] initially applied the RBF network, whose neuron model and network structure are illustrated in Figure 1.…”

Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

et al. 2016

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Based on logistic regression (LR) and artificial neural network (ANN) methods, we construct an LR model, an ANN model and three types of a two-stage hybrid model. The two-stage hybrid model is integrated by the LR and ANN approaches. We predict the credit risk of China's small and medium-sized enterprises (SMEs) for financial institutions (FIs) in the supply chain financing (SCF) by applying the above models. In the empirical analysis, the quarterly financial and non-financial data of 77 listed SMEs and 11 listed core enterprises (CEs) in the period of 2012-2013 are chosen as the samples. The empirical results show that: (i) the "negative signal" prediction accuracy ratio of the ANN model is better than that of LR model; (ii) the two-stage hybrid model type I has a better performance of predicting "positive signals" than that of the ANN model; (iii) the two-stage hybrid model type II has a stronger ability both in aspects of predicting "positive signals" and "negative signals" than that of the two-stage hybrid model type I; and (iv) "negative signal" predictive power of the two-stage hybrid model type III is stronger than that of the two-stage hybrid model type II. In summary, the two-stage hybrid model III has the best classification capability to forecast SMEs credit risk in SCF, which can be a useful prediction tool for China's FIs.

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Section: Artificial Neural Network (Ann) Modelmentioning

confidence: 99%

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

et al. 2016

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“…6). The first stage preprocesses bio-crystallogram red pepper single centered images, and the second stage converts these black and white images into row vectors and estimates Gram-Charlier coefficients [11] corresponding to these row vectors (see Fig. 7).…”

Section: Image Neural Network Architecturementioning

confidence: 99%

Discrimination of bio-crystallogram images using neural networks

Ünlütürk

Unluturk

Pazır

et al. 2013

Neural Comput & Applic

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This study utilized a unique neural network model for texture image analysis to differentiate the crystallograms from pairs of fresh red pepper fruits from conventional and organic farms. The differences in visually analyzed samples are defined as the distribution of crystals on the circular glass underlay, the thin or thick structure of crystal needles, the angles between branches and side needles, etc. However, the visual description and definition of bio-crystallogram images has major disadvantages. A novel methodology called an image neural network (INN) has been developed to overcome these shortcomings. The 1,488 9 2,240 pixel bio-crystallogram images were acquired in a lab and cropped to 425 9 1,025 pixel images. These depicted either a conventional sweet red pepper or an organic sweet red pepper. A set of 19 images was utilized to train the image neural network. A new set of 4 images was then prepared to test the INN performance. Overall, the INN achieved an average recognition performance of 100 %. This high level of recognition suggests that the INN is a promising method for the discrimination of bio-crystallogram images. In addition, Hinton diagrams were utilized to display the optimality of the INN weights.

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“…Predicting the corrections for UTC(PL) based on neural networks requires a training process. It depends on the number of input training data and the data preparation method [19]. Data preparation for the GMDH neural network was based on the historical results of measurements of the phase time between UTC(PL) and Cs2 clock (Fig.…”

Section: Data Preparation For Gmdh Neural Networkmentioning

confidence: 99%

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

Luzar¹,

Sobolewski²,

Miczulski³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

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Probabilistic Neural Networks

Cited by 265 publications

References 0 publications

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

Predicting China’s SME Credit Risk in Supply Chain Financing by Logistic Regression, Artificial Neural Network and Hybrid Models

Discrimination of bio-crystallogram images using neural networks

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

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