past system using neural networks

... Tracking (PAST) System Using Neural Networks 122 xv List of Tables LIST OF TABLES Page Table 5.1: Training from Initial Set of Weights DC motor Position and Speed Tracking (PAST) System Using Neural Networks ... the neural network DC Motor Position and Speed Tracking (PAST) System Using Neural Networks 26 Chapter 3: Position and Speed Tracking (PAST) System CHAPTER Position and Speed Tracking (PAST) System ... (PAST) System Using Neural Networks vii Summary SUMMARY The aim of this thesis is to develop a high performance, position and speed tracking (PAST) system for a DC motor using an artificial neural...

Ngày tải lên: 04/10/2015, 10:25

Ngày tải lên: 03/09/2012, 15:54

... feature-based design system with dynamic editing, Computers and Industrial Engineering, 32(2):383-397 Smith, S.D.G., et al., (1997) A deployed engineering design retrieval system using neural networks, ... similar artifact can be created 7.1.1 Information Retrieval Systems vs Design Retrieving Systems An information retrieval system is a system that is capable of storage, retrieval, and maintenance ... memory with back-propagation neural networks and adaptive resonance theory (Bahrami et al., 1995) Lin and Chang (1996) combine fuzzy set theory and back-propagation neural networks to deal with uncertainty...

Ngày tải lên: 17/12/2013, 06:15

... box’ approach Process Overview Using Neural Networks in HYSYS Using Neural Networks in HYSYS Steps for using Neural Networks in HYSYS The procedure for using Neural Networks in HYSYS is as follows: ... and case studies Using Neural Networks in HYSYS Neural Networks What is a Neural Network? A Neural Network (strictly an ‘Artificial Neural Network’ as opposed to a ‘Biological Neural Network’) ... could include large errors Neural Networks will not predict the effect of changes in variables not included in the training data 11 Using Neural Networks in HYSYS Exercise Using the Parametric Unit...

Ngày tải lên: 23/03/2014, 02:20

... inaccurate, handicapping the system s performance We will see that neural networks help to avoid this problem 1.2 Neural Networks Connectionism, or the study of artificial neural networks, was initially ... speaking rates, etc 1.2 Neural Networks now being focused on the general properties of neural computation, using simplified neural models These properties include: • Trainability Networks can be taught ... recognition using neural networks The remainder of the thesis describes our own research, evaluating both predictive networks and classification networks as acoustic models in NN-HMM hybrid systems:...

Ngày tải lên: 28/04/2014, 10:18

... inaccurate, handicapping the system s performance We will see that neural networks help to avoid this problem 1.2 Neural Networks Connectionism, or the study of artificial neural networks, was initially ... speaking rates, etc 1.2 Neural Networks now being focused on the general properties of neural computation, using simplified neural models These properties include: • Trainability Networks can be taught ... recognition using neural networks The remainder of the thesis describes our own research, evaluating both predictive networks and classification networks as acoustic models in NN-HMM hybrid systems:...

Ngày tải lên: 13/08/2014, 02:21

... that neural networks mitigate each of the above weaknesses (except the First Order Assumption), while they require relatively few parameters, so that a neural network based speech recognition system ... word — so templates are useful only in small systems which can afford the luxury of using whole-word models A more flexible representation, used in larger systems, is based on trained acoustic models, ... namely the ones with the highest scores, using a variation of time alignment called N-best search (Schwartz and Chow, 1990) This allows a recognition system to make two passes through the unknown...

Ngày tải lên: 13/08/2014, 02:21

... extent that the neural network may be simulated on a conventional computer, rather than implemented directly in hardware 3.2 Fundamentals of Neural Networks 29 In drawings of neural networks, units ... Networks In this section we will briefly review the fundamentals of neural networks There are many different types of neural networks, but they all have four basic attributes: • • • • A set of ... the advent of backpropagation, neural networks have enjoyed a third wave of popularity, and have now found many useful applications 3.2 Fundamentals of Neural Networks In this section we will...

Ngày tải lên: 13/08/2014, 02:21

... HMMs Perhaps the simplest way to integrate neural networks and Hidden Markov Models is to simply implement various pieces of HMM systems using neural networks Although this does not improve the ... AlphaNet (final panel) tic modeling in neural networks In particular, neural networks are often trained to compute emission probabilities for HMMs Neural networks are well suited to this mapping ... infancy, and it is premature to rely on neural networks for temporal modeling in a speech recognition system 4.3 NN-HMM Hybrids We have seen that neural networks are excellent at acoustic modeling...

Ngày tải lên: 13/08/2014, 02:21

... speech recognition were performed using an early version of the CMU Conference Registration database (Wood 1992) The database consists of 204 English sentences using a vocabulary of 402 words, ... dialogs (41 sentences) using a reduced vocabulary without a grammar The Conference Registration database was developed in conjunction with the Janus Speech-to-Speech Translation system at CMU (Waibel ... modules — speech recognition, text translation, and speech generation — into a single end-to-end system Each of these modules can use any available technology, and in fact various combinations...

Ngày tải lên: 13/08/2014, 02:21

... predictive networks; their work will be discussed later in this chapter 6.3 Linked Predictive Neural Networks 81 6.3 Linked Predictive Neural Networks We explored the use of predictive networks ... network Nonlinearity is a feature of neural networks in general, hence this is not an advantage of predictive networks over classification networks Although predictive networks yield a whole frame of ... phoneme model that uses only networks, sharing distributions across states Another way to share parameters, which is unique to neural networks, is to collapse multiple networks into a single network,...

Ngày tải lên: 13/08/2014, 02:21

... seen that good word recognition accuracy can be achieved using neural networks that have been trained as speech classifiers However, the networks cannot be simply thrown at the problem; they must ... increasing computational requirements Of course, neural networks can be made not only context-sensitive, but also contextdependent like HMMs, by using any of the techniques described in Sec 4.3.6 ... it is not specifically related to neural networks Therefore we did not make a great effort to optimize our speech models Most of our experiments were performed using 61 context-independent TIMIT...

Ngày tải lên: 13/08/2014, 02:21

... hybrid systems (first three entries) consistently outperformed the pure HMM systems (CI-Sphinx and CI-Decipher), using a comparable number of parameters This supports our claim that neural networks ... The systems in this table are as follows: • MLP: our best multilayer perceptron using virtually all of the optimizations in Chapter 7, except for word level training The details of this system ... other systems except the most primitive HMM, suggesting that predictive networks suffer severely from their lack of discrimination On the other hand, the HCNN (which is also based on predictive networks) ...

Ngày tải lên: 13/08/2014, 02:21

... (1987) Neural Classifiers Useful for Speech Recognition In 1st International Conference on Neural Networks, IEEE [78] Lippmann, R (1989) Review of Neural Networks for Speech Recognition Neural ... temporal pattern recognition based on neural networks If and when that happens, it may become possible to design systems that are based entirely on neural networks, potentially further advancing ... for Training Neural Networks IEEE Trans on Neural Networks, 3(2), March 1992 [7] Barto, A., and Anandan, P (1985) Pattern Recognizing Stochastic Learning Automata IEEE Transactions on Systems, Man,...

Ngày tải lên: 13/08/2014, 02:21

... the next network until the system can recognize it correctly Figure 3: Convolution neural network with unknown output Figure 4: Recognition System using multi neural networks This solution overcomes ... more networks; we can also add new networks to the system to recognize new patterns without change or rebuilt the model All these small networks have reusable capacity to an other multi neural networks ... new recognition system using Spell checker /voting module The new recognition system using Spell checker /voting module (internal dictionary) The spellchecker module makes the system recognizes...

Ngày tải lên: 28/04/2014, 10:11

... for programming of the artificial neural network DESIGN ARTIFICIAL NEURAL NETWORK MODEL VERIFICATIONS OF MANN MODEL Neural networks are computer models that mimic the knowledge acquisition and ... backpropagation neural network model for predicting proper strain rate involved three phases First, data collection phase involved gathering the data for use in training and testing the neural network ... the training patterns was minimized Experiment were carried out using a number of combinations of input parameters to determine the neural network model that gave the smallest average of the sum...

Ngày tải lên: 22/03/2013, 15:01

... estimation for nonlinear dynamical systems and also as a basis for on-line learning algorithms for feedforward neural networks [15] and radial basis function networks [16, 17] For more details, ... DYNAMICAL SYSTEMS USING EM Figure 6.7 More examples of fitting systems with nonlinear dynamics and linear observation functions Each of the five rows shows the fitting of a nonlinear system with ... for all the example systems (Notice that for system E, the linear dynamical system is much better than factor analysis because of the strong hysteresis (mode-locking) in the system Thus, the output...

Ngày tải lên: 23/12/2013, 07:16

... control system that predicts surface roughness during the execution of the machining process These two learning methodologies are artificial neural networks (ANN) and fuzzy neural (FN) systems ... turning carbide inserts using neural networks, Int J Mach Tools Manuf., 36(7), pp 789-797 ©2001 CRC Press LLC Huang, S J and Chiou, K C., 1996 The application of neural networks in self-tuning ... 16 Neural Networks and Neural- Fuzzy Approaches in an In-Process Surface Roughness Recognition System for End Milling Operations 16.1 16.2 16.3 16.4...

Ngày tải lên: 23/01/2014, 01:20

... NEURAL NETWORKS AND INTELLECT This page intentionally left blank NEURAL NETWORKS AND INTELLECT Using Model-Based Concepts Leonid I Perlovsky New ... Perlovsky, Leonid I Neural networks and intellect : using model-based concepts / Leonid I Perlovsky p cm Includes bibliographical references and index ISBN 0-19-511162-1 Neural networks (computer ... in Grossberg’s ART neural network, in the concept of neural field theory, and in similar concepts of other neural networks It is a striking conclusion that philosophers of the past have been closer...

Ngày tải lên: 03/04/2014, 12:09

... weights [10] The application of artificial neural networks (ANN) to the B-WIM was attempted in 2003 by Gonzalez et al for noise removal and calibration of the system [11], and in 2005 as a research ... independently from other WIM systems simultaneously The high-speed WIM system which was developed by a research project conducted by Korean Highway Corporation, and low-speed the WIM system for overweight ... Conclusions In this study, the applicability of artificial neural networks (ANN) is investigated for the improvement of conventional B-WIM systems so that it can be implemented on long-span bridges...

Ngày tải lên: 28/04/2014, 10:02