COMPARISON OF TWO OPTIMIZATION NEURAL NETWORK MODELS

A completely different approach of connectionist learning uses sensory information for robot neuralcontrol. Sensor-based control is a very efficient method in overcoming problems with robot modeland environment uncertainties, because sensor capabilities help in the adaptation proces withoutexp[r]

on SP2 supercomputer and on the NIAS (UNIX)machine at Griffith University. SP2 is an IBM-basedproduct that consists of eight RS/6000 390 machines and14 RS/6000 590 machines connected by a high speedswitch. The operation system is UNIX. Theprogramming language used for implementation was C.Thi[r]

i (i=1 m) and a Global Frame (GF) consisting L Components Neural Network CNNj (j=1 L). Each of SNN uses to process the responsive feature vector. Each of CNN use to combine the responsive element of SNN’s output vector. In fact, the weight coefficients in CNNj are[r]

Chapter 027. Aphasia, Memory Loss, and Other Focal Cerebral Disorders (Part 2) THE LEFT PERISYLVIAN NETWORK FOR LANGUAGE: APHASIAS AND RELATED CONDITIONS Language allows the communication and elaboration of thoughts and experiences by linking them to arbitrary symbols known as words.[r]

of the process dynamics often does not exist. Lack of the physical models makes the design of a processcontroller difficult, and it is virtually impractical to use the conventional control methodologies. In thissituation, these are two widely accepted methods of<[r]

of the process dynamics often does not exist. Lack of the physical models makes the design of a processcontroller difficult, and it is virtually impractical to use the conventional control methodologies. In thissituation, these are two widely accepted methods of<[r]

1.1.5 Dynamic ProgrammingDynamic programming refers to a computational method involving recur-rence relations. This technique was developed by Richard Bellman in theearly 1950’s. It arose from studying programming problems in which changesover time were important, thus the name “dynamic programming”[r]

b i o m a s s a n d b i o e n e r g y 4 9 ( 2 0 1 3 ) 2 7 9 e2 8 9variables, forced the authors to develop four ANNs, one foreach biomass feedstock considered. Even the results showedthat the ANNs developed could reflect the real gasificationprocess; it would have been more interesting to develop ju[r]

Recurrent Neural Networks for PredictionAuthored by Danilo P. Mandic, Jonathon A. ChambersCopyrightc2001 John Wiley &amp; Sons LtdISBNs: 0-471-49517-4 (Hardback); 0-470-84535-X (Electronic)3Network Architectures forPrediction3.1 PerspectiveThe architecture, or structure, of[r]

showninFig.5.55.An artificial neural network is no more than an interconnection of PEs. The formof the interconnectionprovides one of the key variables for dividingneuralnetworksinto families. The mostgeneralcase is the fully connectedneuralnetwork.By defini-tion, any PE ca[r]

FCT on the recognition rate of the proposed technique. Anew par ameter, called the axis correction ratio (ACR), is de-fined to eliminate irrelevant data from the face images andto create a subimage for further feature extraction. We haveshown how ACR can improve the recognition rate. Once thef[r]

６． Determining the error function７． Training８． Implementation. In performing the above steps, it is not necessary to perform steps sequentially. We could be back to the previous steps, especially in training and choosing variables steps. The reason is because in the designing period, if the va[r]

Using random graph terminology [3], disintegration can beseen as a phase transition from degradation—when degradingperformance crosses a threshold beyond which the quality ofservice becomes unacceptable.Network models can be divided into two categories accord-ing to their genera[r]

under real time with various test conditions. It provides the UAV developer to test many aspects of autopilot hardware, finding the real time problems, test the reliability, and many more. The simulation can be done with the help of Matlab Simulink program environment. This program can[r]

expressed by any analytical equation. The neural network canFig. 12. Validation of model with online operating data (phase A).provide very good mapping if trained correctly. This makes it agood choice for such a task.Once the NN is trained, it can estimate the exciting currentfr[r]

L

operational conditions, that affect frequency and three-phase voltage signals. Among the main disturbances that indicate a poor power quality, the following can be highlighted: voltage sag/swell, overvoltage, undervoltage, interruption, oscillatory transient, noise, flicker and harmonic distortion ([r]

routing in network layer and power management in physicallayer. Subsequently, distributed cross-layer algorithms areproposed in order to obtain the solution in the newframework. It should be mentioned that one of the mainfeatures of our method is providing a distributed and para[r]

ging, named entity recognition, sentiment analysisand paraphrase detection, among others. The mostattractive quality of these techniques is that they canperform well without any external hand-designed re-sources or time-intensive feature engineering. De-spite these advantages, many researcher[r]

opimize many properties (Y) of the formulation and is suitable for the problems with complicated and non-linear data.
In this study, a combination of neural networks, fuzzy logic and genetic algorithms (GA) called Soft-Computing (SC) is empl[r]