Neural Network-based Fuzzy Inference System Research Articles

Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing

In this study, fuzzy approach with fault-tolerance has proposed to fuse heterogeneous sensed data and overcome the problem of imprecise collision warning due to perturbed input signal when processing the pre-crash warning. Meanwhile, another problem relevant to the danger in drowsy driving, involving fatigue level, carbon monoxide concentration, and breath alcohol concentration, was considered and has approximately reasoned to an extra reaction time to modify NHTSA algorithm. A vision-sensing analysis cooperating with global-positioning system is applied for lane marking detection and collision warning, particularly exchanging the dynamic and static information between neighboring cars via inter-vehicle wireless communications. In addition to pre-crash warning, event data recording very useful for accident reconstruction on scene is also established here. In order to speed up data fusion on both quantum-tuned back-propagation neural network (QT-BPNN) and adaptive network-based fuzzy inference system (ANFIS), a distributed dual-platform DaVinci+XScale_NAV270 has been employed. Several tests on system’s reliability and validity have been done successfully, and the comparison of system effectiveness showed that our proposed approach outperforms two current well-known collision-warning systems (AWS-Mobileye and ACWS-Delphi).

Feedforward neural network and adaptive network-based fuzzy inference system in study of power lines

Over the past several decades, concerns have been raised over the possibility that the exposure to extremely low frequency electromagnetic fields from power lines may have harmful effects on human and living organisms. This paper presents novel approach based on the use of both feedforward neural network (FNN) and adaptive network-based fuzzy inference system (ANFIS) to estimate electric and magnetic fields around an overhead power transmission lines. An FNN and ANFIS used to simulate this problem were trained using the results derived from the previous research. It is shown that proposed approach ensures satisfactory accuracy and can be a very efficient tool and useful alternative for such investigations.

Desulfurization process using Takagi–Sugeno–Kang fuzzy modeling

Two Takagi–Sugeno–Kang fuzzy models for the prediction of the amount of reagents for desulfurization in steel processing are developed from experimental data. For the design of the models, an algorithm was proposed to be used in the procedures of the two phases: structure building and parametric identification. In the first phase, the Gustafson–Kessel clustering algorithm with the cluster validity index was proposed to find the number of fuzzy rules and an initial fuzzy model. In the second phase, a gradient-descent-based approach was developed and used for optimized tuning of membership functions of the fuzzy model. The numerical results were compared with a conventional statistical model and neural networks and adaptive network-based fuzzy inference system.

Adaptive modeling of the drying of baker's yeast in a batch fluidized bed

This study investigates the drying of baker's yeast in a fluidized-bed dryer. Mathematical modeling of the process was performed, incorporating the important process and quality parameters of the system. Artificial neural network (ANN) and adaptive neural network-based fuzzy inference system (ANFIS) structures were used to create process and quality models. Due to uncertainty regarding the process parameters, various different ANN structures were built, and the ANN with the optimum performance results for the proposed models was selected. This study also presents an ANFIS modeling approach with adaptive structure. ANN quality modeling was performed using process output parameters, and the quality loss incurred from drying the product was determined. These proposed models are easy to apply and do not impose any additional burden on the process (or the employees). The database used in this work was gathered from large quantities of industrial data (about 570 batches) obtained under various working conditions at random times over one year.

A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis

An organization has to make the right decisions in time depending on demand information to enhance the commercial competitive advantage in a constantly fluctuating business environment. Therefore, estimating the demand quantity for the next period most likely appears to be crucial. This work presents a comparative forecasting methodology regarding to uncertain customer demands in a multi-level supply chain (SC) structure via neural techniques. The objective of the paper is to propose a new forecasting mechanism which is modeled by artificial intelligence approaches including the comparison of both artificial neural networks and adaptive network-based fuzzy inference system techniques to manage the fuzzy demand with incomplete information. The effectiveness of the proposed approach to the demand forecasting issue is demonstrated using real-world data from a company which is active in durable consumer goods industry in Istanbul, Turkey.

Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system

Coagulation is an important component of water treatment. Determining the optimal coagulant dosage is vital, as insufficient dosage will result in unqualified water quality. Traditionally, jar tests and operators’ own experience are used to determine the optimum coagulant dosage. However, jar tests are time-consuming and less adaptive to changes in raw water quality in real time. When an unusual condition occurs, such as a heavy rain, the storm water brings high turbidity to water source, and the treated effluent quality may be inferior to drinking water quality standards, because the conventional operation method can be hardly in time to adjust to the proper dosage. An optimal modeling can be used to overcome these limitations. In this paper, artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models were used to model poly aluminum chloride (PAC) dosing of northern Taiwan's surface water. Each of them was built based on 819 sets of process-controlled data. The performance of the models was found to be sufficient. Two simulation tools, ANN and ANFIS, were developed that enabled operators to obtain real-time PAC dosage more easily. The self-predicting model of ANFIS is better than ANN for PAC dosage predictions.

Visual evoked potential estimation by adaptive noise cancellation with neural-network-based fuzzy inference system

Visual evoked potentials (VEPs) are time-varying signals typically buried in relatively large background noise known as the electroencephalogram (EEG). In this paper, an adaptive noise cancellation with neural network-based fuzzy inference system (NNFIS) was used and the NNFIS was carefully designed to model the VEP signal. It is assumed that VEP responses can be modelled by NNFIS with the centres of its membership functions evenly distributed over time. The weights of NNFIS are adaptively determined by minimizing the variance of the error signal using the least mean squares (LMS) algorithm. As the NNFIS is dynamic to any change of VEP, the non-stationary characteristics of VEP can be tracked. Thus, this method should be able to track the VEP. Four sets of simulated data indicate that the proposed method is appropriate to estimate VEP. A total of 150 trials are processed to demonstrate the superior performance of the proposed method.

Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks

This paper illustrates the application of artificial neural networks (ANN) for prediction of pesticide occurrence in rural domestic wells from the available limited information. Among the three ANN models (a feed-forward back propagation [BP], a radial basis function [RBF] and an adaptive neural network-based fuzzy inference system [ANFIS]) employed for this investigation, the BP neural network was found to be superior to RBF and ANFIS type networks for the detection of pesticide occurrences in wells. For improved model prediction efficiency, optimization of network structure (e.g., number of hidden layers and number of nodes in each hidden layer) and spread (the width of the radial basis function) are important for BP and RBF type of network, respectively. A four layer BP network with a 3:2 neurons ratio of the first hidden layer to the second hidden layer produced better prediction performance efficiencies in terms of the pesticide detection efficiency ( E f), the root mean square error (RMSE), and the correlation coefficient ( R) and the overall E f of the BP neural network was found greater than 85%. Sensitivity analysis was performed to measure the relative importance of one input parameter over the other in pesticide occurrence in wells. It was shown in terms of the prediction efficiencies ( E f, RMSE, and R) of a four-layer BP neural network that the time of sample collection (TSC; month of the year), the depths of wells, and pesticide travel times (PTT) were more important parameters in the prediction of the pesticide occurrences in rural domestic wells. This means that the wells having shallow ground water table are more vulnerable to pesticide occurrence.

Application of adaptive noise cancellation with neural-network-based fuzzy inference system for visual evoked potentials estimation

This paper presents an application of adaptive noise cancellation with neural-network-based fuzzy inference system (NNFIS) for rapid estimation of visual evoked potentials (VEPs). Usually a recorded VEP is severely contaminated by background ongoing activities of the spontaneous EEG signal in the human brain. Many approaches have been adopted to enhance the signal-to-noise ratio (SNR) of the recorded signal. However, nonlinear dynamic methods are rarely investigated in view of their complexity, and the fact that the nonlinear characteristics of the signal are hard to determine in general. An adaptive noise cancellation method with NNFIS was carefully designed to estimate the VEP signal. NNFIS, based on Takagi and Sugeno’s fuzzy model, has the advantage of being linear-in-parameter; thus the conventional adaptive methods can be efficiently utilized to estimate its parameters. Another advantage of NNFIS lies in that it can track the dynamic behavior of VEP in a real-time fashion because the VEP variation tracking is important for critical patient monitoring in the clinical situation. A series of computer experiments conducted on simulated and real-test responses have confirmed the superiority of the method developed in this paper.

Tool condition monitoring using artificial intelligence methods

This paper describes an application of three artificial intelligence (AI) methods to estimate tool wear in lathe turning. The first two are “conventional” AI methods—the feed forward back propagation neural network and the fuzzy decision support system. The third is a new artificial neural network based-fuzzy inference system with moving consequents in if–then rules. Tool wear estimation is based on the measurement of cutting force components. This paper discusses a comparison of usability of these methods in practice.