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Optimizing the traffic signal control has an essential impact on intersections efficiency in urban transportation. This paper presents a two-stage method for intersection signal timing control. First, the traffic volume is predicted using a neuro-fuzzy network called Adaptive neuro-fuzzy inference system (ANFIS). The inputs of this network include two-dimensional, hourly and daily, traffic volume correlations. In the second stage, appropriate signal cycle and optimized timing of each phase of the signal are estimated using a combination of Self Organizing and Hopfield neural networks. The energy function of the Hopfield network is based on a traffic model derived by queuing analysis. The performance of the proposed method has been evaluated for real data. The two-dimensional correlation presents superior performance compared to hourly traffic correlation. The evaluation of proposed overall method shows considerable intersection throughput improvement comparing to the results taken form Synchro software. [ABSTRACT FROM AUTHOR]

The neuro-fuzzy refers to the recent technology that couples the traditional fuzzy logic developments with neural nets training capabilities to compose the fuzzy logic’s knowledge base and fuzzy sets’ parameters optimally. This paper discusses the calibration methodology of a neuro-fuzzy logic for modeling the route choice behavior. The logic accounts for the various factors of potential effect on the route choice utility perceived by the traveler. The structure of the fuzzy control stages, the calibration of the membership functions, and the composition of the knowledge base are discussed in detail. Logic training is based on data extracted from a factorial experimental design model. The results of the fuzzy logic model are utilized for in-depth analyses of the travelers’ perceptions of the route utility in response to the various traffic states. [ABSTRACT FROM AUTHOR]

This paper develops a representation of multi‐model based controllers by using artificial intelligence typical structures. These structures will be neural networks, genetic algorithms and fuzzy logic. The interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented. The proposed methodology allows to extend the existing single model based neural controllers to multimodel based ones, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms and fuzzy logic to multimodel controller design are proposed. Thus, the mutation operation from genetic algorithms inspires a robustness test which consists of a random modification of the estimates which is used to select the estimates leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classical multimodel schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multiestimation structures which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm for creating rules of a fuzzy logic system using a neuro-fuzzy approach. The proposal is based on the results of Juang and Tsao who use a Fuzzy Neural Network (FNN) to generate rules and fuzzy sets from input data. A time series example is solved using our proposal, which is tested using statistical analysis of the residuals of the model. [ABSTRACT FROM AUTHOR]

Scale is highly detrimental to surface quality for tinplate products. There are a large number of process variables at a typical hot mill and principal component analysis is a well-known technique for reducing the number of process variables. This paper estimates the principal components associated with the hot mill process variables and puts these through an Adaptive Neuro Fuzzy Inference System (ANFIS) to find those hot mill running conditions that will minimise the amount of scale observed on the bottom of the rolled strip. It was found that the variation observed in all the hot mill process variables could be captured through the use of just six principal components, and that using just three of these in an ANFIS was sufficient to identify those operating conditions leading to coils being produced with a consistently low scale count. Specifically, it was found that the best operating conditions for the hot mill were when the first component was lower than -0.098 the second lower than 0.8058 and the third higher than -0.482. These ranges in turn corresponded to certain hot mill temperatures that depended to some extent on the base chemistry of the incoming slab. [ABSTRACT FROM AUTHOR]

Abstract: The use of mathematical tools such as Artificial Neural Networks and Fuzzy Logic has been shown to be useful for solving complex engineering problems, without the need to reproduce the phenomenon under study, when the only information available consists of the parameters of the problem and the desired results. Based on a collection of 96 laboratory tests, this paper uses Artificial Neural Networks and Fuzzy Logic to determine the compressive strength of a masonry structure composed of clay bricks and cement mortar, by using only two parameters: the compressive strength of the mortar and that of the bricks. These mathematical techniques are an alternative to the complex analytical formulas dependent on a large number of parameters and to empirical formulas, which, even though simple, often give unrealistic values. The results obtained are compared to the calculation methods proposed by other authors and other standards and demonstrate the suitability of using Neural Networks and Fuzzy Logic to predict the compressive strength of masonry. [Copyright &y& Elsevier]

Summary: This paper proposes the application of evolutionary fuzzy models (EFMs) for suspended sediment concentration estimation. The EFMs are improved by the combination of two methods, fuzzy logic and differential evolution. The accuracy of EFMs is compared with those of the adaptive neuro-fuzzy, neural networks and rating curve models. The daily streamflow and suspended sediment data belonging to two stations, Quebrada Blanca Station and Rio Valenciano Station, operated by the US Geological Survey (USGS) are used as case studies. The mean square errors and determination coefficient statistics are used for evaluating the accuracy of the models. Based on the comparison of the results, it is found that the EFMs give better estimates than the other techniques. [Copyright &y& Elsevier]

The complexity of microbial processes, coupled with the effects of incomplete mixing and the influx of noise, makes it difficult to describe quantitatively the behavior of bioreactors under realistic conditions. By circumventing the need for phenomenological equations, neural networks offer viable descriptions of such systems. However, such networks also require large amounts of carefully screened data and long training schedules. They also have limited extrapolation capability and poor physiological fidelity. Since phenomenological models are derived from physical descriptions of cellular processes, combinations of such models with neural networks, and sometimes also fuzzy logic, have provided good portrayals of nonideal bioreactor behavior while reducing the weaknesses of both approaches. However, like neural networks themselves, different architectures are possible for hybrid neural models. The rationale, architectures and illustrative applications of hybrid models for biological reactors are discussed here, with the possibility of combining them with cybernetic models, exploiting cellular intelligence, to develop self-evolving intelligent systems for optimization and control of microbial processes. [ABSTRACT FROM AUTHOR]

This research presents a Bayesian artificial intelligence model averaging (BAIMA) method that incorporates multiple artificial intelligence (AI) models to estimate hydraulic conductivity and evaluate estimation uncertainties. Uncertainty in AI model outputs stems from errors in model input and nonuniqueness in selecting different AI methods. Using one single AI model tends to bias the estimation and underestimate uncertainty. The BAIMA employs a Bayesian model averaging (BMA) technique to address the issue of using one single AI model for estimation. The BAIMA estimates hydraulic conductivity by averaging the outputs of AI models according to their model weights. In this study, the model weights are determined using the Bayesian information criterion (BIC) that follows the parsimony principle. The BAIMA calculates the within-model variances to account for uncertainty propagation from input data to AI model output. Between-model variances are evaluated to account for uncertainty because of model nonuniqueness. The authors employ Takagi-Sugeno fuzzy logic (TS-FL), an artificial neural network (ANN), and neuro-fuzzy (NF) to estimate hydraulic conductivity for the Tasuj plain aquifer, Iran. The BAIMA combines three AI models and produces better fitting than individual models. Although NF was expected to be the best AI model owing to its utilization of both the TS-FL and ANN models, the NF model is nearly discarded by the parsimony principle. The TS-FL model and the ANN model show equal importance, although their hydraulic conductivity estimates are quite different. This results in significant between-model variances that are normally ignored by using one AI model. [ABSTRACT FROM AUTHOR]

The complexity of microbial processes, coupled with the effects of incomplete mixing and the influx of noise, makes it difficult to describe quantitatively the behavior of bioreactors under realistic conditions. By circumventing the need for phenomenological equations, neural networks offer viable descriptions of such systems. However, such networks also require large amounts of carefully screened data and long training schedules. They also have limited extrapolation capability and poor physiological fidelity. Since phenomenological models are derived from physical descriptions of cellular processes, combinations of such models with neural networks, and sometimes also fuzzy logic, have provided good portrayals of nonideal bioreactor behavior while reducing the weaknesses of both approaches. However, like neural networks themselves, different architectures are possible for hybrid neural models. The rationale, architectures and illustrative applications of hybrid models for biological reactors are discussed here, with the possibility of combining them with cybernetic models, exploiting cellular intelligence, to develop self-evolving intelligent systems for optimization and control of microbial processes. [ABSTRACT FROM AUTHOR]

The present study aims at the application of the hybrid model, which consists of artificial neural network and fuzzy logic in the reservoir operating policy during critical periods. The proposed hybrid model [fuzzy neural network (FNN)] combines the learning ability of artificial neural networks and the transparent nature of fuzzy logic. The FNN model is found to be highly adaptive and efficient in investigating nonlinear relationships among different variables. The FNN model has been developed to study the behavior of optimal release operating policy on the proposed reservoir in Pagladiya River of the Assam State in India. Here, reservoir operation policies were formulated through dynamic programming. The optimal release was related to storage, inflow, and demand. The advantages of using the FNN model in reservoir release are discussed using the case study. [ABSTRACT FROM AUTHOR]