Neural Networks And Fuzzy Logic Pdf Free Download

Neuro-fuzzy hybridization results in a hybrid intelligent system that combines the human-like reasoning style of fuzzy systems with the learning and connectionist structure of neural networks. Neuro-fuzzy hybridization is widely termed as fuzzy neural network (FNN) or neuro-fuzzy system (NFS) in the literature. Neuro-fuzzy system (the more popular term is used henceforth) incorporates the human-like reasoning style of fuzzy systems through the use of fuzzy sets and a linguistic model consisting of a set of IF-THEN fuzzy rules. The main strength of neuro-fuzzy systems is that they are universal approximators with the ability to solicit interpretable IF-THEN rules.

The "POPFNN" architecture is a five-layer neural network where the layers from 1 to 5 are called: input linguistic layer, condition layer, rule layer, consequent layer, output linguistic layer. The fuzzification of the inputs and the defuzzification of the outputs are respectively performed by the input linguistic and output linguistic layers while the fuzzy inference is collectively performed by the rule, condition and consequence layers.

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Neural Network:

Neural network is an information processing system that is inspired by the way biological nervous systems such as brain process information. A neural network is composed of a large number of interconnected processing elements known as neurons which are used to solve problems. A neural network is an attempt to make a computer model of the human brain and neural networks are parallel computing devices. The simple diagram of the neural network is as shown below:

The principal objective of this subject is to introduce students to neural networks and fuzzy theory from an engineering perspective. This is a hands-on subject where students are given integrated exposure to professional practice. These areas include identification and control of dynamic systems, neural networks and fuzzy systems can be implemented as model-free estimators and/or controllers. As trainable dynamic systems, these intelligent control systems can learn from experience with numerical and linguistic sample data. As an example, students will develop an expertise in biomedical, pattern recognition, control system using neural networks and fuzzy logic.

A fuzzy neural network or neuro-fuzzy system is a learning machine that finds the parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by exploiting approximation techniques from neural networks.

Both neural networks and fuzzy systems have some things in common. They can be used for solving a problem (e.g. pattern recognition, regression or density estimation) if there does not exist any mathematical model of the given problem. They solely do have certain disadvantages and advantages which almost completely disappear by combining both concepts.

Neural networks can only come into play if the problem is expressed by a sufficient amount of observed examples. These observations are used to train the black box. On the one hand no prior knowledge about the problem needs to be given. On the other hand, however, it is not straightforward to extract comprehensible rules from the neural network's structure.

It is desirable for fuzzy systems to have an automatic adaption procedure which is comparable to neural networks. As it can be seen in Table 1, combining both approaches should unite advantages and exclude disadvantages.

Compared to a common neural network, connection weights and propagation and activation functions of fuzzy neural networks differ a lot. Although there are many different approaches to model a fuzzy neural network (Buckley and Hayashi, 1994, 1995; Nauck and Kruse, 1996), most of them agree on certain characteristics such as the following:

In the case of cooperative neural fuzzy systems, both artificial neural network and fuzzy system work independently from each other. The ANN tries to learn the parameters from the fuzzy system. This can be either performed offline or online while the fuzzy system is applied. Figure 2 depicts four different kinds of cooperative fuzzy neural networks.

The upper left fuzzy neural network learns fuzzy set from given training data. This is usually performed by fitting membership functions with a neural network. The fuzzy sets are then determined offline. They are then utilized to form the fuzzy system by fuzzy rules that are given (not learned) as well.

The upper right neuro-fuzzy system determines fuzzy rules from training data by a neural network. Here as well, the neural networks learns offline before the fuzzy system is initialized. The rule learning usually done by clustering on self-organizing feature maps (Bezdek et al., 1992; Vuorimaa, 1994). It is also possible to apply fuzzy clustering methods to obtain rules.

The lower right one determines rule weights for all fuzzy rules by a neural network. This can be done online and offline. A rule weight is interpreted as the influence of a rule (Kosko, 1992). They are multiplied with the rule output. In (Nauck et al., 1997) the authors argue that the semantics of rule weights are not clearly defined. They could be replaced by modified membership functions. However, this could destroy the interpretation of fuzzy sets. Moreover, identical linguistic values might be represented differently in dissimilar rules.

Hybrid neuro-fuzzy systems are homogeneous and usually resemble neural networks. Here, the fuzzy system is interpreted as special kind of neural network. The advantage of such hybrid NFS is its architecture since both fuzzy system and neural network do not have to communicate any more with each other. They are one fully fused entity. These systems can learn online and offline. Figure 3 shows such a hybrid FNN.

The rule base of a fuzzy system is interpreted as a neural network. Fuzzy sets can be regarded as weights whereas the input and output variables and the rules are modeled as neurons. Neurons can be included or deleted in the learning step. Finally, the neurons of the network represent the fuzzy knowledge base. Obviously, the major drawbacks of both underlying systems are thus overcome.

In order to build a fuzzy controller, membership functions which express the linguistic terms of the inference rules have to be defined. In fuzzy set theory, there does not exist any formal approach to define these functions. Any shape (e.g., triangular, Gaussian) can be considered as membership function with an arbitrary set of parameters. Thus the optimization of these functions in terms of generalizing the data is very important for fuzzy systems. Neural networks can be used to solve this problem.

An important hybrid fuzzy neural network has been introduced in (Berenji, 1992). The ARIC (approximate reasoning-based intelligent control) is presented as a neural network where a prior defined rule base is tuned by updating the network's prediction. Thus the advantages of fuzzy systems and neural networks are easily combined as presented in Table 1.

The ARIC is represented by two feed-forward neural networks, the action-state evaluation network (AEN) and the action selection network (ASN). The ASN is a multilayer neural network representation of a fuzzy system. It then again consists of two separate. The first one represents the fuzzy inference and the second one computes a confidence measure based on the current and next system state. Both parts are eventually combined to the ASN's output.

The AEN (which is as three-layer feed-forward neural network as well) aims to forecast the system behavior. The hidden layer obtains as input both the system state and an error signal from the underlying system. The output of the networks shall represent the prediction of the next reinforcement which depends on the weights and the system state. The weights are changed by a reinforcement procedure which takes into consideration the outputs of both networks ASN and AEN, respectively. ARIC was successfully applied to the cart-pole balancing problem.

Whereas the ARIC model can be easily interpret as a set of fuzzy-if-then rules, the ASN network to adjust the weights is rather difficult to understand. It is a working neural network architecture that utilizes aspects of fuzzy systems. However, a semantic interpretation of some learning steps is not possible.

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow. Artificial neural network (ANN), fuzzy logic, and adaptive neuro-fuzzy inference system (ANFIS) algorithms are used to develop nine different flood prediction models using all the available training algorithms. The performance of the developed models is evaluated using multiple statistical performance evaluators. The predictability and robustness of the models are tested through the simulation of a major flood event in the study area. A total of 12 inputs were used in the development of the models. Five training algorithms were used to develop the ANN models (Bayesian regularization, Levenberg Marquardt, conjugate gradient, scaled conjugate gradient, and resilient backpropagation), two fuzzy inference systems to develop fuzzy models (Mamdani and Sugeno), and two training algorithms to develop the ANFIS models (hybrid and backpropagation). The ANFIS model developed using hybrid training algorithm gave the best performance metrics with Nash-Sutcliffe Model Efficiency (NSE) of 0.968, coefficient of correlation (R2) of 97.066%, mean square error (MSE) of 0.00034, root mean square error (RMSE) of 0.018, mean absolute error (MAE) of 0.0073, and combined accuracy (CA) of 0.018, implying the potential of using the developed models for flood forecasting. The significance of this research lies in the fact that a combination of multiple inputs and AI algorithms has been used to develop the flood models. In summary, this research revealed the potential of AI algorithm-based models in predicting floods and also developed some useful techniques that can be used by the Flood Control Departments of various states/regions/countries for flood prognosis. e24fc04721