Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on physically locating specific landmarks, it is a boring, lengthy and error prone task. The objective of this work is to calculate the SNA angle, SNB angle and ANB angle between the landmarks to identify the input and output parameters pertaining to skeletal abnormalities. By doing s...

The performance of recently developed neural network structure, general regression neural network (GRNN), is examined on the medical data. Pima Indian Dabetes (PID) data set is chosen to study on that had been examined by more complex neural network structures in the past. The results of early studies and of the GRNN structure presented in this paper is compared. Close classification accuracy t...

The main purpose of this study is to devise a general regression neural network (GRNN)based currency crisis forecasting model for Southeast Asian economies based upon the disastrous 1997–1998 currency crisis experience. For this some typical indicators of currency exchange rates volatility are first chosen, then these indicators are input into GRNN for training, and finally the trained GRNN is ...

In this article, a PSO-SVR-GRNN nonparametric hybrid model is proposed for the CSI 300 stock index to forecast problem. Particle Swarm Optimization (PSO) utilized optimize parameters of SVR enhance prediction ability support vector machine's regression original Index time series. The optimized residual sequence results General Regression Neural Network (GRNN) are then used series prediction. ou...

Generalized Regression Neural Network (GRNN) is usually applied to the Function approximation. This paper, based on the principle of GRNN, presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the learning and predicting process for the system modeling. The simulations show the described method has good effects on predicting the dynamic pr...

This paper presents a new approach for state adequacy evaluation of sampled system state in composite power system reliability analysis. Generalized regression neural network (GRNN) is used in conjunction with non-sequential Monte Carlo simulation (MCS) to evaluate the loss of probability and the power indices. GRNN approach predicts the test functions for all the sampled states after sufficien...

Alternative forms of neural networks have been applied to forecast daily river flows on a continuous basis with the purpose of understanding how recent architectures like ANFIS, GRNN and RBF compare with traditional FFBP when monsoon-fed rivers involving significant statistical bias are involved. The forecasts are made at a location called Rajghat along river Narmada in India. Division of yearl...

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is propo...

The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as ...

Comparison with the classical BP neural network, the generalized regression neural network requires not periodic training process but a smoothing parameter. The model has steady and fast speed, and meanwhile, the connection weight of different neurons is not necessary to be adjusted in the training process. The paper establishes the index system of GRNN forecasting model, and then uses Bayes th...