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One benefit of **fuzzy** **systems** (Zadeh, 1965; Ruspini et al., 1998; Cox, 1994) is that the rule base can be created from expert knowledge, used to specify **fuzzy** sets to partition all variables and a sufficient number of **fuzzy** rules to describe the input/output relation of the problem at hand. However, a **fuzzy** system that is constructed by expert knowledge alone will usually not perform as required when it is applied because the expert can be wrong about the location of the **fuzzy** sets and the number of rules. A manual tuning process must usually be appended to the design stage which results in modifying the membership functions and/or the rule base of the **fuzzy** system. This tuning process can be very time- consuming and error-prone. Also, in many applications expert knowledge is only partially available or not at all. It is therefore useful to support the definition of the **fuzzy** rule base by automatic learning approaches that make use of available data samples. This is possible since, once the components of the **fuzzy** system is put in a parametric form, the **fuzzy** **inference** system becomes a parametric model which can be tuned by a learning procedure. **Fuzzy** logic and artificial neural **networks** (Haykin, 1998; Mehrotra et al., 1997) are complementary technologies in the design of intelligent **systems**. The combination of these two technologies into an integrated system appears to be a promising path toward the development of Intelligent **Systems** capable of capturing qualities characterizing the human brain. Both neural **networks** and **fuzzy** logic are powerful design techniques that have their strengths and weaknesses. Table 1 shows a **comparison** of the properties of these two technologies (Fuller, 2000). The integrated system will have the advantages of both neural **networks** (e.g. learning abilities, optimization abilities and connectionist structures) and **fuzzy** **systems** (humanlike IF-THEN rules thinking and ease of incorporating expert knowledge) (Brown & Harris,1994). In this way, it is possible to bring the low-level learning and computational **power** of neural **networks** into **fuzzy** **systems** and also high-level humanlike IF-THEN thinking and reasoning of **fuzzy** **systems** into neural **networks**. Thus, on the neural side, more and more transparency is pursued and obtained either by pre- structuring a neural network to improve its performance or by possible interpretation of the weight matrix following the learning stage. On the **fuzzy** side, the development of methods allowing automatic tuning of the parameters that characterize the **fuzzy** system can largely draw inspiration from similar methods used in the connectionist community. This combination does not usually mean that a neural network and a **fuzzy** system are used together in some way.

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presented. Brushless DC motors (BLDC) find wide applications in industries due to their high **power** density and ease of control.To achieve desired level of performance the motor requires suitable speed controllers. The mathematical model of BLDC motor and a back propagation **Adaptive** **Neuro**-**Fuzzy** **Inference** **Systems** (ANFIS) algorithm are considered and included to replace the conventional method of Proportional Integral and **Fuzzy**. ANFIS it integrates both neural **networks** and **fuzzy** logic principles, it has potential to capture the benefits of both in a single framework. Its **inference** system corresponds to a set of **fuzzy** IF–THEN rules that have learning capability to approximate nonlinear functions. Hence, ANFIS is considered to be a universal estimator. The analysis of overshoot, rise time and steady **state** error for the speed range which indicates that the proposed **adaptive** **neuro**-**fuzzy** **inference** **systems** has successfully improved the performance of the BLDC motor drive. According to new proposed approach speed control of BLDC motor drive and analysis using **adaptive** **Neuro**-**Fuzzy** **inference** **systems** to carry off the weakness of **fuzzy** logic controller (Steady-**state** error).Further the ANFIS controller provides low torque ripples and high starting torque. The proposed ANFIS controller is evaluated by using MATLAB/SIMULINK software.

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Abstract— This paper presents a high performance tracking method for maximum **power** generated by photovoltaic (**PV**) **systems**. Based on **adaptive** **Neuro**-**Fuzzy** **inference** **systems** (ANFIS), this method combines the learning abilities of artificial neural **networks** and the ability of **fuzzy** logic to handle imprecise data. It is able to handle non-linear and time varying problems hence making it suitable for accurate maximum **power** point tracking (MPPT) to ensure **PV** **systems** work effectively. The performance of the proposed method is compared to that of a **fuzzy** logic based MPPT algorithm to demonstrate its effectiveness.

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soft computing and clustering, that potentially represent viable solutions to this problem, have been developed over the years. The term soft computing describes the collective or singular use of many different computing methods such as Artificial Neural **Networks** (ANN), **Fuzzy** **Inference** **Systems** (FIS) and **Adaptive** **Neuro**-**Fuzzy** **Inference** **Systems** (ANFIS) and many more [19]. By its nature, soft computing is tolerant of imprecision and uncertainty and does not require exact input to output matching. This makes it ideal for the load model problem, where correlation between the **power** consumption and information on variable external factors may be vague and nonlinear [19]. Complimentary to this, clustering is defined by Jain et al, as “the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters)”, and while not easily usable as a standalone classification and **prediction** tool, clustering processes are inherently used within certain soft computing methods to generate a starting rule base on which optimisation permutations may be performed [20].

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Abstract: **Adaptive** **Neuro**-**Fuzzy** **Inference** System is growing to predict nonlinear behaviour of construction materials. However due to wide variety of parameters in this type of artificial intelligent machine, selecting the proper optimization methods together with the best fitting membership functions strongly affect the accuracy of **prediction**. In this study the non- linear relation between splitting tensile strength and modulus of elasticity with compressive strength of high strength concrete is modelled and the effect of different effective parameters of **Adaptive** **Neuro**-**Fuzzy** **Inference** System is investigated on these models.

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In this paper, a novel spectrum sensing method is proposed depending on the artificial intelligence for indentifying the spectrum. This spectrum sensing method based on the ANFIS algorithm which is principally exploited to identify the borders of the subband and recognize the spectrum holes in specified input band. In contradiction to a simple energy detector, the detector depending on the proposed ANFIS can predict the **state** at a future time instant. The experimental results based on real spectrum measurement data reveal that the ANFIS based detector leads to more accurate **state** estimation and **prediction** than other detectors, predominantly in circumstances with high path loss and/or strong shadowing effects. Additionally, the method was evaluated against three methods from the literature in this field, in two of the considered scenarios. This proposed approach showed success percentages comparable with those achieved by the other approaches. Further work will be directed to a statistical analysis to get an optimal setting of the three thresholds used in the method.

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Furthermore, neural **networks** possess a variety of alternative features such as massive parallelism, distributed representation and computation, generalization ability, adaptability and inherent contextual information processing. On the other hand, **fuzzy** sets constitute the oldest and most reported soft computing paradigm. They are well suited to modelling different forms of uncertainties and ambiguities, often encountered in real life. The objective of the hybridization through ANFIS has been to overcome the weaknesses in one technology during its application, with the strengths of the other by appropriately integrating them.

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Second, mobile devices are always “hungry” for energy and computing resources (e.g., limitations of CPU, memory, and user interfaces), so anti-phishing tools are usually ignored or removed on these devices. Hence, it is hard for users to discern if an incoming link is legitimate or not. Third, existing anti-phishing tools (e.g., default plug-ins on web browsers or local anti-phishing applications) are inefficient in terms of detection (this will be analyzed concretely later in Section III), and mobile users may be exposed to phishing attacks when engaging in usual behaviors. According to the report [5], mobile users are three times more likely to submit their login information than desktop users do. Therefore, preventing phishing attacks against terminal users is a critical issue in the edge of **networks**.

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Training parameters is a problem in the design of a NFN. To solve this problem, back-propagation (BP) training is widely used [3]-[8]. It is a powerful training technique that can be applied to **networks**. Nevertheless, the steepest descent technique is used in BP training to minimize the error function. The algorithm may allow the local minima to be reached very quickly, yet the global solution may never be found. In addition, the performance of BP training depends on the initial values of the system parameter, and for different network topologies, new mathematical expressions for each network layer have to be derived. Considering the disadvantages mentioned above, one might be faced with suboptimal performances, even for a suitable NFN topology. Hence, techniques capable of training network parameters and finding a global solution while optimizing the overall structure are needed.

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An **adaptive** **neuro**-**fuzzy** **inference** system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic noise estimation at eight locations. The results have been compared and analyzed with observed noise levels and the coefficient of co-relation between observed and predicted noise level was found to be in range of 0.70 to 0.95. The model performance has also been compared with Federal Highway Administration (FHWA), Calculation of road traffic noise (CRTN) and regression noise models and it is observed that the model performs better than conventional statistical noise model. The proposed noise model is completely generalized and problem independent so it can be easily modified to **prediction** traffic noise under various traffic criteria and serve as first hand tool for traffic noise assessment.

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The neurofuzzy network used in the structure of the proposed hybrid filter acts like a mixture operator and attempts to construct an enhanced output image by combining the information from the new tri-**state** median (NTSM) filter. The rules of mixture are represented by the rules in the rule base of the **neuro**-**fuzzy** network and the mixture process is implemented by the **fuzzy** **inference** mechanism of the **neuro**- **fuzzy** network. These are described in detail later in this subsection. The **neuro**-**fuzzy** network is a first order Sugeno type **fuzzy** system [49] with one input and one output. In **neuro**-**fuzzy** network, there are two types of **fuzzy** **inference** **systems** are widely used. Mamdani method is widely accepted for capturing expert knowledge. It allows us to describe the expertise in more intuitive, more human-like manner. However, mamdani-type **fuzzy** **inference** entails a substantial computational burden. On the other hand, the Sugeno method is computationally effective and works well with optimization and **adaptive** techniques, which makes it very attractive in control problems, particularly for dynamic nonlinear **systems**. Sugeno-type **fuzzy** **systems** are popular general nonlinear modeling tools because they are very suitable for tuning by optimization and they employ polynomial type output membership functions, which greatly simplifies defuzzification process. The input-output relationship of the **neuro**-**fuzzy** network is as follows. Let A 1 denote the inputs of the **neuro**-

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metrological data and (2) Make **fuzzy** and **neuro** **fuzzy** model and their results will be evaluated. The **fuzzy** rule- based approach is applied for the construction of **fuzzy** models. To remove the weaknesses of **fuzzy** models that are not trained during the modeling, **adaptive** **neuro**-**fuzzy** **inference** system (ANFIS) with given input/output data sets will be use for **neuro**-**fuzzy** model. To do this, **Fuzzy** models was studied with input parameters such as daily maximum and minimum temperature, relative humidity percent, sunshine hours, wind speed. As well as output of this **fuzzy** system such as Evapotranspiration will studied in this research. After determining effective parameters in

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The palmprint image is decomposed into an approximate image and three detail images with the **adaptive** scheme. Then PCNN is used to decompose each detail image into 30 binary images. In this the number of neurons in the network is equal to the number of input image. One-to-one connection exists between image pixels and neurons [4]. Each pixel is connected to a unique neuron and each neuron is connected with the surrounding neurons with a radius of linking field. The neuron receives input signals from other neurons and from external sources through the receptive fields. After the receptive fields have collected the inputs, they are divided into two or more internal channels. One channel is the feeding input F and the other is the linking input L. The feeding connections are required to have a slower characteristic response time constant than those of the linking inputs. The linking inputs are biased and then multiplied together, and further multiplied with the feeding input to form the total internal activity U. The pulse generator of the neuron consists of a step function generator and a threshold signal signal generator. At each time step the neuron output Y is set to 1 when the internal activity U is greater than the threshold function T. The threshold input at each time step is updated. The output of the neuron is consequently reset to zero when T is larger than U. Thus at one time step the pulse generator produces a single pulse at its output whenever the value of U exceeds T.

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The hydrologic behavior of rainfall-runoff process is very complicated phenomenon which is controlled by large number of climatic and physiographic factors that vary with both the time and space. The relationship between rainfall and resulting runoff is quite complex and is influenced by factors relating the topography and climate. In recent years, artificial neural network (ANN), **fuzzy** logic, genetic algorithm and chaos theory have been widely applied in the sphere of hydrology and water resource. ANN have been recently accepted as an efficient alternative tool for modeling of complex hydrologic **systems** and widely used for **prediction**. Some specific applications of ANN to hydrology include modeling rainfall-runoff process. **Fuzzy** logic method was first developed to explain the human thinking and decision system by [1]. Several studies have been carried out using **fuzzy** logic in hydrology and water resources planning [2]. **Adaptive** **neuro**- **fuzzy** **inference** system (ANFIS) which is integration of neural **networks** and **fuzzy** logic has the potential to capture the benefits of both these fields in a single framework. ANFIS utilizes linguistic information from the **fuzzy** logic as well learning capability of an ANN. **Adaptive** **neuro** **fuzzy** **inference** system (ANFIS) is a **fuzzy** mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) **fuzzy** **inference** system [3] and [4]. ANFIS used for many applications such as, database management, system

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Platt and Platt [3], and Cheng et al. [4] used a Logit model to analyze pre-warning model and to a build financial distress model, while Zhang et al. [5], and O’leary [6] used artificial neural **networks**. One problem of the logistic regression is that serial correlation might exist in the explanatory variables. Another problem is the inconsistency generated from the errors on construction of the dummy variable indicating the financial stage, distress or stability periods, crisis or no crisis periods, leading to misclassification of time points. With ANFIS approach we do not face these problems, which are very usual in conventional econometric modelling. A significant study was made by Cheng et al. [7]. The authors study a pre-warning financial distress model for the TSE listed companies and they apply a binary logit and a **fuzzy** regression model with triangular membership function. Their results support **fuzzy** regression, where the correctly predicted percentage of **fuzzy** regression is 90.98 percent versus logit regression which predicts correctly the 90.30 percent. In this paper we expand this approach by taking panel data as we have a group of companies. Because we have various companies among time periods we need to examine logistic regressions through panel data analysis and to investigate if random or fixed effects are more appropriate. With this approach we show that the overall percentage, and especially the correct percentage of financial distress periods, of panel Logit model is significant higher to simple binary Logit model without panel data analysis examined by Cheng et al. [7]. Additionally, we propose ANFIS because the overall correct classified percentage of financial distress and stability periods is significant superior to Logit and **fuzzy** regressions.

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The main motivation for applying a **Neuro**- **fuzzy** computing approach is that it combines the generalization capabilities of Neural **Networks** with the ease of interpretability and high expressive **power** of **fuzzy** rules in an effective way. Vibration signals were obtained from the Feed Pump using Tri-axial Accelerometer and FFT. These signals were processed in MATLAB using ANFIS tool for training, testing and checking to simulate the Feed Pump. The performance criterion of the ANFIS classifier was evaluated using confusion matrix. The total classification accuracy of 95% obtained, proves the validation of the Feed Pump model. **Neuro**-**Fuzzy** **Systems** have high potential in diagnosis of machinery. The proposed ANFIS model has been found to be an effective tool for diagnosing faults.

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It has been known for decades that chaotic behaviors exist in traffic flow **systems** Gazis et al. [1] developed a generalized car-following model, known as the GHR (Ga- zis-Herman-Rothery) model, whose discontinuous beha- vior and nonlinearity suggested chaotic solutions for a certain range of input parameters. Due to the capacity di- mension [2] of the attractor being fractal and first Lyapu- nov exponent [3] being positive, Disbro and Frame [4] showed the presence of chaos in this General Motors’ mo- del without signals, bottlenecks, intersections, etc. or with a coordinated signal network. Chaos was observed in a platoon of vehicles described by the traditional GHR mo- del modified by adding a nonlinear inter-car separation dependent term [5,6], Poincaré maps of which appear as a cloud of points without any repeat. Traffic volume col- lected at 2-min interval on the Beijing Xizhimen highway, China, was also found to posses chaotic behaviors [7].

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Abstract: Climate change impacts and adaptations is subject to ongoing issues that attract the attention of many researchers. Insight into the wind **power** potential in an area and its probable variation due to climate change impacts can provide useful information for energy policymakers and strategists for sustainable development and management of the energy. In this study, spatial variation of wind **power** density at the turbine hub-height and its variability under future climatic scenarios are taken under consideration. An ANFIS based post-processing technique was employed to match the **power** outputs of the regional climate model with those obtained from the reference data. The near-surface wind data obtained from a regional climate model are employed to investigate climate change impacts on the wind **power** resources in the Caspian Sea. Subsequent to converting near-surface wind speed to turbine hub-height speed and computation of wind **power** density, the results have been investigated to reveal mean annual **power**, seasonal, and monthly variability for a 20-year period in the present (1981-2000) and in the future (2081-2100). The results of this study revealed that climate change does not affect the wind climate over the study area, remarkably. However, a small decrease was projected for future simulation revealing a slightly decrease in mean annual wind **power** in the future compared to historical simulations. Moreover, the results demonstrated strong variation in wind **power** in terms of temporal and spatial distribution when winter and summer have the highest values of **power**. The findings of this study indicated that the middle and northern parts of the Caspian Sea are placed with the highest values of wind **power**. However, the results of the post-processing technique using **adaptive** **neuro**-**fuzzy**

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cordings have been obtained from PhysioNet apnea-ECG database. Wavelet transforms have been applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10 th and 11 th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings have been used for training and testing of an **adaptive** **neuro** **fuzzy** **inference** system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for ge- neralization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. How- ever the best result has been obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of en- tire data. Due to the results of the 1-minute based analy- sis, the classification accuracies of ANFIS have obtained between 80.6% - 81.5%, 89.2% - 90.9%, 90.8% - 92.9% and 88.6% - 90.4% respectively for the chest, nasal, ab- dominal respiratory and EDR signals. For the analysis of 3-minute length data, the classification accuracies have obtained between 84.8% - 86.5%, 95.2% - 96.5%, 93.4% - 95.4% and 92.0% - 94.0%, respectively. According to these results, both of the 1-minute and 3-minute length of chest, nasal, abdominal based respiratory and EDR sig- nals can be used sufficiently for proposed method. How- ever the best result can be obtained by analyzing the sec- tion of the 3-minute length nasal based respiratory Table 1. ANFIS based classification accuracies for the analysis of 1-minute length signal.

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