Neural network -based system

The end users of CBR systems should in principle be able to maintain the case-bases themselves and use the systems for varying inspection types (within certain limits). Adaptation of neural-network based systems, though possible by end-users, is difficult to be done reliably. Adaptation of rule-based systems usually has to be done by the rule-base designer. 

M. Norgaard, Neural network based system identification toolbox. Technical report. Institute of Automation, Technical University, Denmark, 1995. 

Dey D, Munshi S (2008) An artificial neural network based system for measurement of humidity and temperature using capacitive humidity sensor and thermistor. Sens Transducers J 97(10) 1-10 Di Francia G, Noce MD, Ferrara VL, LanceUotti L, MorvUlo P, Quercia L (2002) Nanostractured porous silicon for gas sensor application. Mater Sci Technol 18 767-771... 

The purpose of this case study was to develop a simple neural network based model with the ability to predict the solvent activity in different polymer systems. The solvent activities were predicted by an ANN as a function of the binary type and the polymer volume frac-... 

Coupling Fast Variable Selection Methods to Neural Network-Based Classifiers Application to Multi-Sensor Systems. 

Models for biochemical switches, logic gates, and information-processing devices that are also based on enzymic reactions but do not use the cyclic enzyme system were also introduced [76,115,117-122]. Examples of these models are presented in Table 1.3. It should also be mentioned that in other studies [108,112-114,116], models of chemical neurons and chemical neural networks based on nonenzymic chemical reactions were also introduced. 

A neural-network-based simulator can overcome the above complications because the network does not rely on exact deterministic models (i.e., based on the physics and chemistry of the system) to describe a process. Rather, artificia] neural networks assimilate operating data from an industrial process and learn about the complex relationships existing within the process, even when the input-output information is noisy and imprecise. This ability makes the neural-network concept well suited for modeling complex refinery operations. For a detailed review and introductory material on artificial neural networks, we refer readers to Himmelblau (2008), Kay and Titterington (2000), Baughman and Liu (1995), and Bulsari (1995). We will consider in this section the modeling of the FCC process to illustrate the modeling of refinery operations via artificial neural networks. 

Lee, S. C., D. V. Heinbuch, Training a Neural-Network Based Intrusion Detector to Recognize Novel Attacks, IEEE Trans, on Systems, Man, and Cybernetics, Part A, 31, 2001, pp. 294-299. 

The architecture of an ANFIS model is shown in Figure 14.4. As can be seen, the proposed neuro-fuzzy model in ANFIS is a multilayer neural network-based fuzzy system, which has a total of five layers. The input (layer 1) and output (layer 5) nodes represent the descriptors and the response, respectively. Layer 2 is the fuzzification layer in which each node represents a membership. In the hidden layers, there are nodes functioning as membership functions (MFs) and rules. This eliminates the disadvantage of a normal NN, which is difficult for an observer to understand or to modify. The detailed description of ANFIS architecture is given elsewhere (31). 

R M 2000, the Air Force Reliability and Maintainability Action Plan does not discuss the unique problems (and potential) of expert systems, particularly those which rely on the massive "knowledge bases" which can now be delivered on optical disc. Nor have they addressed the problems (and potential) associated with parallel processing, neural networks and "systems that learn". 

Neural network approaches have been used as an alternative to other nonhnear techniques for modeling physiological systems [Chon et al., 1998]. Several neural network control systems have utilized model-based approaches in which the neural network is used to identify a forward nonlinear system... 

A biomedical control system that utilizes a neurophysiologically-based approach has been developed for use in Functional Neuromuscular Stimulation (FNS) systems [Abbas, 1995 Abbas and Chizeck, 1995). FNS is a rehabilitation engineering technique that uses computer-controlled electrical stimuli to activate paralyzed muscle. The task of a control system is to determine appropriate stimulation levels to generate a given movement or posture. The neural network control system utilizes a block diagram structure that is based on hierarchical models of the locomotor control system. It also utilizes a heterogenous network of neurons, some of which are capable of endogenous oscillation. This network has been shown to provide rapid adaptation of the control system parameters [Abbas and Chizeck, 1995 Abbas and Triolo, 1997] and has been shown to exhibit modulation of reflex responses [Abbas, 1995]. 

This chapter presents an overview of the relatively new field of neural network control systems. A variety of techniques are described and some of the advantages and disadvantages of the various techniques are discussed. The techniques described here show great promise for use in biomedical engineering applications in which other control systems techniques are inadequate. Currently, neural network control systems lack the type of theoretical foundation upon which linear control systems are based, but recently... 

Lin, C.T. and Lee, C.S.G., Neural-network-based fuzzy logic control and decision system, IEEE Trans. Comput. C-40 1320-1336,1991. 

Dengyou Xia. 2007. Fire Risk Evaluation Model of High-Rise Buildings Based on Multilevel BP Neural Network. Fuzzy Systems and Knowledge Discovery 24-27. 

Wong, W.K., Zeng, X.H. and Au, W.M.R., 2009. A decision support tool for apparel coordination through integrating the knowledge-based attribute evaluation expert system and the T-S fiizzy neural network. Expert Systems with Applications, 36(2), 2377-2390. 

After introduction a prototype of intelligent multi-sensor system for driver status monitoring— DeCaDrive is presented in Section 2. The system expansion with embedded impedance spectroscopy sensor, its analog front-end and sensor data preprocessing are addressed in Section 3. Multi-sensor feature computation and data fusion as well as neural network based pattern classification are discussed in Section 4. The extended system is validated and evaluated by presenting the experimental results in Section 5. Finally, with future perspectives the current work is concluded in Section 6. 

PAYA, B. A. ESAT, I. I. (1997) Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms asapreprocessor. Mechanical Systems and Signal Processing, 11 (5), pp. 751-765. 

Having successfully implemented conventional MRAC techniques, the next logical step was to try to incorporate the MRAC techniques into a neural network-based adaptive control system. The ability of multilayered neural networks to approximate linear as well as nonlinear functions is well documented and has foimd extensive application in the area of system identification and adaptive control. The noise-rejection properties of neural networks makes them particularly useful in smart structure applications. Adaptive control schemes require only limited a priori knowledge about the system to be controlled. The methodology also involves identification of the plant model, followed by adaptation of the controller parameters based on a continuously updated plant model. These properties of adaptive control methods makes neural networks ideally suited for both identification and control aspects [7-11]. 

The neural network-based control algorithm described in Sect. 4.4 is tested using simulation studies on a cantilever plate system with PZT actuators and PVDF film sensors. A top-view line diagram of the plate structure is shown in Fig. 4.2. 

In this section, a neural network-based design methodology is developed that utilizes the adaptability of neural networks to compensate for the time varying dynamical properties of smart structures. This formulation is designed to be implemented using the ETANN chip and also allows the designer to directly incorporate all the a priori information about the system that may be available. An important feature of this formulation is that it relies only on the experimental input/output data of the system for the design. The ability of neural networks to map nonlinear systems allows this formulation to be extended to incorporate nonlinearity in structural systems. 

Various neural network-based adaptive control techniques were discussed in this study. A major problem in implementing neural network-based MRACs is the translation of the output error between the plant and the reference model so as to train the neural controller. A technique called iterative inversion, which inverts the neural identification model of the plant for calculating neural controller gains, has been used. Due to the real-time computer hardware limitations, the performance of neural network-based adaptive control systems is verified using simulation studies only. These results show that neural-network based MRACs can be designed and implemented on smart structures. 

A neural network-based control algorithm based on a LQ performance index which can be implemented using the ETANN chip has been developed. This formulation incorporates a priori information about the structural system. Information such as limits on the control effort and limits on the bandwidths of the sensors and actuators can be incorporated in this... 

Keller, P.E., Kouzes, R.T., and Kangas, L.J. (1994) Three neural network based sensor systems for environmental monitoring. IEEE Electro 94 Conference Proceeding, Boston, MA, pp. 377-382. 

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