Radial basis function

(2010) 743–750 Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks Changhao Xia a,b,*, Jian Wang b,*, Karen McMenemy c a College of Electrical Engineering and Information Technology, China Three Gorges University, Yichang Hubei 443002, China School of Mechanical and Aerospace Engineering

Physical examination revealed a loud fourth heart sound and a blood pressure of 147/95 mmHg. Chest examination and chest X-ray were unremarkable, and ECG showed left atrial abnormality. The patient had normal serum electrolytes, blood sugar, and kidney function tests. A stress echocardiogram was ordered to exclude potential coronary artery disease (CAD). His resting echocardiography showed an ejection fraction (EF) of 60%, normal septal and posterior wall thickness, and mild diastolic dysfunction [septal

of comparison of different window function requires a cumbersome

CHAPER 5 : SIMULATION / EXPERIMENTAL RESULT 5.1 RESULTS USING NEURAL NETWORK – GA STRUCTURE : - To check the performance of genetic neural network .Firstly the dataset normalized and divided into groups of training and testing set .Randomly generated the groups by splitting process with specific proportion at each time(50% to 50%,10% to 90%,30% to 70%) to formdifferent training /testing groups. GA applied five times for finding the optimal network topology For each testing /training pairwhich

window function using artificial neural network (ANN). The optimization of the neural network model has been done using a generalized regression algorithm (GRNN). Here the radial basis function is used for the training of the neural network and compared with GRNN. [3] Suruchi Sharma and Abhishek Lahariya used artificial neural network (ANN) for the design of the low pass FIR filter. The Blackman window has been used for this purpose. Feed forward distributed time delay (FFDTD) and radial basis

This is due to the different layouts of the spatial boundaries used in model fitting and prediction, and therefore is challenging to be addressed by the existing modelling strategies. Hence, in this paper we develop a method based on spatial basis functions that comprises properties of both GMRF and the Gaussian spatial random field. In

RFID AND FACE RECOGNITION BASED ACCESS CONTROL SYSTEM 1Kenward Dzvifu, 2T Chakavarika Department of Information Security & Assurance, Harare Institute of Technology, Zimbabwe 1kenwarddzvifu@gmail.com 2ttchaka@gmail.com School of Information Science and Technology, Harare Institute of Technology, Zimbabwe ABSTRACT— The Radio frequency identification (RFID) technology has been broadly adopted in access control systems. This technology is based on the use of a card or tag and has some major

before you will see any significant results. There has been a relationship developing between Ginkgo as an anti aging aid since it is such a powerful antioxidant that wards off the free radials. It is very common and becoming even more so for healthy people to include Ginkgo as an herbal supplement on a daily basis for better concentration and enhanced memory. People claim that in general they feel that Ginkgo reduces any mental fatigue that daily life has a tendency to bring forth. Many men are

non-linear classification by using kernel functions, implicitly mapping their inputs into high-dimensional spaces. This makes it a suitable tool in predicting the compressive strength of concrete which is non-linearly related to its mix ingredients. In the SVM model, the training data is first mapped onto an m-dimensional feature space by using non-linear mapping. The output can be written by 13 constructing a linear model in this feature space. The output function can be written in mathematical form as:

is to find the output weights . It is solved by reducing the training error and also the rule of the output weights. If the norms of weights and training error are smaller, then the generalization performance of SLFNs tends to get better. The main function can be termed as . To solve the above problem, smallest norm least square method is used. where, is the Moore-Penrose generalized inverse of matrix Q. Several methods are there to calculate such as orthogonalization method, orthogonal projection