Time delay neural network

If we denote as f the set of all mappings from the input space of /-dimensional vectors into the output space of o-dimensional vectors that fulfil the restrictions (i) and (ii) listed in Subsection 6.1.4., then training neural networks without delay can be considered as nothing else than time evolution of computed mapping within the set f. In the case of supervised discrete learning, that evolution has several specific features ... 

In Figure 10.30 the predietive neural network model traeks the ehanging dynamies of the plant. Following a suitable time delay, em(kT) is passed to the performanee index table. If this indieates poor performanee as a result of ehanged plant dynamies, the rulebase is adjusted aeeordingly. Riehter (2000) demonstrated that this teehnique eould improve and stabilize a SOFLC when applied to the autopilot of a small motorized surfaee vessel. 

Waibel, A., Hanazawa, T., Hinton, G., Shikano, K. Lang K..J. 1989. Phoneme recognition using time-delay neural networks. IEEE Transactions On Acoustics, Speech, and Signal Processing, 37/3, 328-339. 

Fig. 10.8 (a) Example of common neural net (perceptron) architecture. Here one hidden layer Neural Networks (NNs) is shown (Hierlemann et al., 1996). (b) A more sophisticated recurrent neural network utilizing adjustable feedback through recurrent variables, (c) Time-delayed neural network in which time has been utilized as an experimental variable... 

Zhang, W., and Dietterich, T. (1996), High-Performance Job-Shop Scheduling with a Time-delay TD(() network, Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, pp. 1025-1030. 

A third model-based approach is neural predictive control, which is a neural network version of nonlinear model predictive control [Trajanoski and Wach, 1998]. In this approach, the neural network is used for ofif-Hne identification of a system model, which is then used to design a nonhnear model predictive controller. This design may provide suitable control of nonlinear systems with time-delays and thus maybe particularly useful in biomedical appHcations. Recent computer simulation studies have demonstrated positive results for control of insuhn dehvery [Trajanoski and Wach, 1998]. 

Muller, A. F., and Hoffmann, R. Accent label prediction by time delay neural networks using gating elusters. a Proceedings of Eurospeech 2001 (2001). 

H. Zhang, M.O. Balaban, J.C. Principe, Improving pattern recognition of electronic nose data with time-delay neural networks. Sens. Actuators B 96,385-389 (2003)... 

A time-delay-neural-network in nonlinear autoregressive with exogenous input structure (narx) for single input/output (one layer) data is chosen for the calculation of an unknown input-output-relation... 

From the structure of a neural network it can be stated that the information learned is stored in the weights of the links. For different problems different stmctures can be used. The links do not have to point in the same direction, recurrent networks, in which delayed outputs are used as inputs, are also very common for process modehng since it can describe time dependency. Also all neurons in a network can be coimected with each other. For this type of network, the input, hidden and output neurons have to be strictly defined. However, the basic principle as explained above is valid for all these networks. 

An important factor in the popularity of feed-forward networks is that it has been shown that a continuous valued neural network with a continuous differentiable non-linear transfer function can approximate any continuous function arbitrarily well (Cybenko, 1989). The feedforward architecture shown in Fig. 27.1 is typically used for steady-state functional approximation or one-step-ahead dynamic prediction. However, if the model is to be used to predict also more than one time step ahead, recurrent nemal networks should be used, in which delayed outputs are used as neuron inputs... 

Clouse, D.S., Giles, C.L., Horne, B.G. and Cottrell, G.W. (1997). Time-delay neural networks representation and induction of finite-state machines, IEEE Trans. Neural Networks, 8, 1065-1070. 

Artificial neural networks (ANNs) are good at classifying non-linearly separable data. There are at least 30 different types of ANNs, including multilayer perceptron, radial basis functions, self-organizing maps, adaptive resonance theory networks and time-delay neural netwoiks. Indeed, the majority of ATI applications discussed later employ ANNs - most commonly, MLP (multilayer perceptron), RBF (radial basis function) or SOM (self-organizing map). A detailed treatise of neural networks for ATI is beyond the scope of this chapter and the reader is referred to the excellent introduction to ANNs in Haykin (1994) and neural networks applied to pattern recognition in Looney (1997) and Bishop (2(X)0). Classifiers for practical ATI systems are also described in other chapters of this volume. 

Keywords— Malay Vowel Recognition, Time Delay Neural Network, Automatic Speech Recognition, Children Speech. 

Fig. 1 Classical Time-Delay Neural Network by Waibel et al. [11]...

Fig. 2 Time delay neural network for Malay children vowel recognition...

Speaker-Independent Vowel Recognition for Malay Children Using Time-Delay Neural Network... 

The speaker independent vowel recognition using Time Delay Neural Network was presented. The result showed that TDNN was capable of discriminate different Malay vowels. The temporal characteristic of the speech was learned properly by the recognition model. Accuracy of 81.92% suggested that the model is suitable for vowel recognition for children. 

Waibel A, Hanazawa T, Hinton G, Shikano K and Lang K J (1989) Phoneme recognition using time-delay neural network. IEEE transaction on acoustics, speech and signal processing 37 328-337... 

Sugiyama M, Sawai H and Waibel A (1991) Review of TDNN (Time-delay neural network) architectures fa- speech recc nition. IEEE international symposium on circuit and systems, 1991, pp 582-585... 

Lang K, Waibel A and Hinton G (1990) A time-delay neural network architecture for isolated word recognition. Neural networks 3(1) 23-43... 

In this work, an inverse nemal network model was proposed. That is, measured residual wall thicknesses at five different positions of molded parts as shown in Figure 2 are the input variables. The output variables consist of fom processing parameters, including melt tenperature, water injection pressure, water injection delay time, and short-shot size. Among 31 data sets obtained from experiments by changing processing parameters, 26 sets of data (data 1 26 shown in Table 1) were used as training patterns to train the LMBP neural network to ascertain its... 

Once trained, the neural network model has been identified and can be utilized to forecast the processing paramrters expected for new levels of residual wall thicknesses at five positions of molded part. For exarrtple, inputting thicknesses 3.75, 3.72, 3.48, 3.65, and 3.00 mm at positions PI to P5 to the model, it can predict the processing parameters as follows melt temperature 232.3°C, water injection pressure 8.6 MPa, water injection delay time 3.4 s, and short-shot size 88.1%. While the actual measrrrements are melt temperature 230°C, wato-injection pressme 9 MPa, water injection delay time 3 s, and short-shot size 85%. 

---