Abstract: **Fuzzy** linear **regression** analysis with symmetric triangular **fuzzy** number coefficient has been introduced by Tanaka et al. In this work we propose to approximate the **fuzzy** **nonlinear** **regression** **using** **Artificial** **Neural** Net- works. The working of the proposed method is illustrated by the case study with the data for temperature and evaporation for the IARI New Delhi division.

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The **artificial** **neural** network (ANN), an **artificial** intelligence technique, is a potential tool for modeling data in poultry production. Roush et al. (1997) used an ANN to make a probabilistic prediction of ascitis in broilers, with no need of post-mortem examinations or other procedures. According to the authors, the developed models improved ascitis diagnosis in broilers. Salle et al. (2001) studied the possibility of **using** ANN methodology to estimate production parameters of developing broiler breeders, and found that this method allowed the simulation of the consequences of management decisions, determining the contribution of each variable to the studied phenomenon.

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The present study uses an **artificial** NN approach for **nonlinear** modelling of multivariate streamflow time-series. This technique is used for synthetic generation of monthly inflows to the two reservoirs. The research follows the lines initiated by several authors in the past, some of whom compared, in practical case studies, linear stochastic models with NN models, while others used mixed models, i.e. NN plus a random noise. Lachtermacher and Fuller (1994) modelled annual streamflow series **using** multi-layer feed- forward error-backpropagation NN, with iterated multi-step prediction, where the single output of the model was used for subsequent forecasting. Then, a Box-Jenkins modelling approach was used to determine the appropriate number of inputs (previous values of past streamflows) in the NN. Raman and Sunilkumar (1995) built twelve different NNs, one for each month of the year, which were then used for streamflow generation for two reservoir sites. They compared the technique with results derived from a bivariate

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Recently, we have worked with LR techniques **using** the same input data, and the results illustrated the importance of terrain roughness and soil type as key factors within the model; **using** only these two variables, the analysis returned a significance level of 89.4% (Garc´ıa-Rodr´ıguez et al., 2008, 2009). In the LR, the probability was estimated according to the logistic formula, which provided a deterministic model for the data and yielded weighted factors for each contribut- ing factor. It also allowed the calculation of the odds ratio, which represents the degree of risk associated with each fac- tor. However, LR fits the data to a fixed function, so it is less flexible and less capable of solving complex problems com- pared to ANNs. The advantages of ANNs and its possible application to the evaluation of landslide susceptibility come from the remarkable information processing characteristics of the **artificial** simulated biological system, including the ability to handle imprecise and **fuzzy** information, fault and failure tolerance, high parallelism, non-linearity, robustness, capability to generalize, and tolerance to noise data (Basheer and Hajmeer, 2000). On the other hand, a disadvantage is that they are known as black-box methods, since it is not known exactly how ANNs learn particular problems and ap- ply the extracted rules to new cases, or how conclusions can be drawn from the trained **networks** (G´omez and Kavzoglu, 2005). Although the accuracy produced in our work is of 95.1% for the ANN versus 89.4% for the LR, the black-box characteristic of the former does not allow for the investiga- tion of which variables are more influential to the response variable. However, this is possible with the LR because the weight of each factor is known.

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Advangeo® provides the software environment for effective data pre-processing, step-by-step model generation, and result visualisation. It is a tool to build up structured and comprehensible models within the widely used ESRI GIS environment. The software will be further developed to improve its usability and functionality. Currently, two other approaches are in the implementation process: Weights of Evidence as an alternative data driven method and **Fuzzy** Logic for knowledge based data modelling.

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Once the ensemble is trained, the topology of the gene regulatory network is obtained by applying a second procedure. Considering each gene in the network separately, we pass a value of 1 to the input neuron of the correspondent multilayer perceptron, consequently recording its output values. The continuous output values in the range ½{1,1 represent the expected normalized expression values for the other genes (its neighborhood). This procedure basically aims at verifying the correlation between the input gene and all the others: assuming the input gene maximally expressed (the value 1), an output value of (i.e.) 1 indicates that the correspondent gene will be also maximally expressed, thus indicating perfect correlation between the two genes. An output value of (i.e.) {1 indicates that the correspondent gene will be maximally under-expressed: perfect anti-correlation of the two genes. Thus, the continuous output values in the range ½{1,1 are interpretable in terms of positive correlation (w0), anti-correlation (v0) and no-correlation (0). By cycling this procedure through all the ensemble members in the **regression** system, we obtain N (one for each of the N genes in the network) vectors of length N{1 of continuos values in ½{1,1. The correlation matrix is obtained by correctly joining the N vectors. It is important to note that all the values of the diagonal of the adjacency matrix are equal to 0 by construction: this procedure does not allow discovering of gene self correlation (regulation) patterns, but only correlation patterns among different genes. Finally the adjacency matrix of the sought gene network is obtained by thresholding the correlation coefficients.

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Abstract. The masonry is not only included among the oldest building mate- rials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly **nonlinear** relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of arti ﬁcial **neural** **networks** for predicting the compressive strength of masonry has been investigated. Speciﬁcally, back-propagation **neural** network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental ﬁndings demonstrates the ability of arti ﬁcial **neural** **networks** to approximate the compressive strength of masonry walls in a reliable and robust manner.

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Protection of the environment from medical waste hazards is becoming a serious problem. There is a big relation between medical waste and disease injury. The main idea of this study is predict the relation between medical wastes and diseases in Hashemite Kingdom of Jordan **using** **Artificial** **Neural** **Networks** (ANNs) model. There are six predictor parameters associated with solid and liquid wastes in the medical services sector which are affecting the diseases injury. This study deals with two types of diseases the first one is acute hepatitis and the other is typhoid. Generalized **Regression** **Neural** Network (GRNN) is used to predict the diseases injury. It is noticed a significant improvement in the prediction made by GRNN due to its generalization property. Results showed that all six parameters associated with solid and liquid medical wastes which have the largest **regression** value affect the acute hepatitis injuries and the typhoid injuries. It is also showed that the medical waste affected the typhoid injuries in large percentage so the **regression** is very large.

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Joseph C. et al. (2006) described the development of a **fuzzy** **neural** network-based in-process mixed material- caused flash prediction (FNN-IPMFP) system for injection molding processes. The goal is to employ a **fuzzy** **neural** network to predict flash in injection molding operations when **using** recycled mixed plastics. Major processing parameters, such as injection speed, melt temperature, and holding pressure, were varied within a small range. The vibration signal data during the mold closing and injection filling stages was collected in real-time **using** an accelerometer sensor. The data was analyzed with **neural** **networks** and **fuzzy** reasoning algorithms, in conjunction with a multiple **regression** model, to obtain flash prediction threshold values under different parameter settings. The FNN-IPMFP system was shown to predict flash with 96.1% accuracy during the injection molding process.

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Now **using** **fuzzy** **regression** which is clarified before, the price of electricity is predicted. In order to perform **fuzzy** **regression** the input variables should be indicated, here the input variables are the same as input nodes of the ANN model. Similar to **neural** network model data have classified in two groups, train data and test data. Train data are used for training model including almost 80% of the data. Test data which are applied for testing the model and including almost 20% of data have used for calculating the errors of the model. Inputs of **fuzzy** **regression** model are the actual electricity price, one hour ago price, the same hour in the previous day price, demand of electricity in that time, and hours of ahead in the same day; these data are crisp data, with the HBS1 model, outputs of the model are **fuzzy** numbers. In this paper after finding the results of the model, the **fuzzy** output defuzzified and the forecasted values are obtained. The obtained result can be seen in fig. 5.

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This work proposes the use of multivariate optimization as a procedure for cadmium determination in leachate samples via flame atomic absorption spectrometry after solid phase extraction **using** a minicolumn packed with Amberlite XAD-4 modified with 3,4-dihydroxybenzoic acid. The variables related with the preconcentration (pH, sampling flow rate and buffer concentration) were optimized **using** Doehlert design. Two statistical modeling tools (least squares **regression** and **artificial** **neural** **networks**) have been applied to the data and their performances were compared. Digestion procedures of the leachate by heating in acid medium and ultraviolet radiation were evaluated being the latter more appropriate to prevent loss of Cd by volatilization. The developed procedure has promoted an enrichment factor of 9, with detection and quantification limits (3s b ) of 0.72 and 2.4 µg L -1 , respectively, and precision - expressed as relative standard

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In this study the most common **neural** network type, the multilayer perceptron, was adopted. This type of network is formed by three or more layers of basic computing units named **artificial** neurons or nodes. It includes an input layer, an output layer and a number of hidden layers with a certain number of active neurons connected by feed forward links, to which are associated modifiable weights. In addition, there are also bias, which are connected to neurons in the hidden and output layers. The number of nodes in the input layer denotes the number of independent variables and the number of nodes in the output layer stands for the number of dependent variables (Haykin, 2008). To obtain the best prediction of the output parameters different network structures and architectures were elaborated and evaluated. The optimum number of hidden layers and the optimum number of nodes in each of these cases was found by trial and error. Mean Square Error (MSE) is used in the current study to compare the performances of **regression** models.

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An ANN is a system inspired by the operation of biological neurons with the purpose of learning a certain system. The construction of an ANN is achieved by providing a stimu- lus to the neuronal model, calculating the output, and adjust- ing the weights until the desired output is achieved. An entry is submitted to the ANN along with a desired target, a de- fined response for the output (when this is the case, the train- ing is regarded as supervised). An error field is built based on the difference between the desired response and the out- put of the system. The error information is used as feedback for the system, which adjusts its parameters in a systematic way; in other words, the backpropagation error algorithm is used to train the network. According to Alsmadi et al. (2009) the backpropagation architecture is the most popular, most effective, and easiest-to-learn model for complex, multilay- ered **networks**. This network is used more than all others combined. This algorithm has a first phase with a functional propagation signal (feedforward) and a second phase with the backpropagation of the error (backpropagation).

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method including texture features is more accurate than maximum algorithm. The comparison of classification in **artificial** and **fuzzy** **neural** **networks** technique has been conducted to separate classes of forest from non-forest **using** SPOT satellite **neural** network method with the accuracy of 64% and kappa coefficient of 0.4 has higher accuracy than **fuzzy** method with the accuracy of 36% and Kappa coefficient of 0.20. Murayama and Khoi in an area at North Vietnam in 2011, modeled the destruction of forests **using** **artificial** **neural** network and Markov chain models (Montserud and Leamans 1992, Khoi and Murayama 2011). This study showed that the degradation in the boundary between forests and agricultural lands, areas close to water sources and areas with lower altitude is more intense. **Neural** network and GIS were used to assess the potential of climate changes effects on a complex landscape of northeastern Queensland tropical forests. Model inputs included variables of climate, soil parent material classes and ground variables. The obtained model was very successful in detecting forest classes with 75% accuracy of prediction. Changes in vegetation have been modeled in Australia **using** two logistic **regression** and **artificial** **neural** **networks** approach (Pijanowski et al. 2001). The results showed that both methods are capable of modeling vegetation changes, however **neural** network has a relatively better performance (Ahamed et al. 2011). The purpose of this study Evaluation of forests mountain use satellite data ETM+ with **neural** Network method in near Caspian

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Coordinate transformation is necessary in the surveying and mapping industry particularly in developing countries like Ghana where the non-geocentric datum which is still utilized is highly heterogeneous. It also creates the opportunity to harmonize coordinates from different reference systems onto a common datum. Firstly, in this study, official parameters (OP) determined by the then DMA now NGA and the estimated arithmetic mean parameters (AM) were applied in the geocentric translation model to transform coordinates from WGS84 datum to Accra datum. Conversely, unsatisfactory coordinate transformation results were achieved by AM and OP in the geocentric translation model application. To improve performance of the geocentric translation model, this study proposed a novel technique known as the **Artificial** **Neural** Network-Error Compensation Model (ANN-ECM) to transform coordinates from WGS84 datum to Accra datum. The ANN-ECM comprise of the BPNN-ECM, RBFNN-ECM and GRNN-ECM. The results obtained showed that the proposed ANN-ECMs are feasible for coordinate transformation within Ghana ’s geodetic reference network. It was also observed that the transformation accuracy of the proposed ANN-ECMs were significantly better than when the geocentric translation model was applied separately. This implies that the proposed ANN-ECMs can better compensate for the errors of the geocentric translation model. In comparison, the BPNN-ECM, RBFNN-ECM and GRNN-ECM produced a maximum horizontal position error of approximately 0.59 m, 0.93 m and 0.9 m respectively. The attained values of the BPNN-ECM and GRNN-ECM are in direct compliance with the Ghana Survey and Mapping Division of Lands Commission horizontal position shift tolerance of ± 0.9144 m for cadastral surveying and plan production in Ghana. Nonetheless, the RBFNN-ECM results could still be used for mapping related activities where accuracy is not in high demand.

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In this section, the stabilization **fuzzy** controller of the spherical mobile robot is described. Here, four state variables must be handled in order to cover the angular control and the position control. These four state variables are the error of position, error of angle, change rate of position error, and change rate of angle error. The output of this controller is the PWM (Pulse Width Modulation) command transmitted to DC motor. If we give five **fuzzy** sets for each input variable then there are 625 **fuzzy** rules in this **fuzzy** controller. It not only consumes a large amount of time to compute the **fuzzy** implication but also we are hard to derive a set of rules and membership functions for this big **fuzzy** rule base. It is also difficult to implement by a single chip.

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finding an appropriate **neural** network, able to simulate the behavior of proximity sensor at different functioning conditions, we choose a high degree of parameterization for the application, with different kind of parameters, which varies in a large range. The second step is to determine the **neural** network [10], based on over 40,000 data sets obtained from multiparametric solving of the numerical model and going through a selection process based on network performance criteria. With network determined in this way, simulations were made on the data sets not used for the training process.

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