In this study, nonlinear autoregressive recurrent **neural** networks with exogenous input (NARX) were used to predict **global** **solar** **radiation** across **New** **Zealand**. Data for nine hourly weather variables recorded across **New** **Zealand** from January 2006 to December 2012 were used to create, train and test **Artificial** **Neural** **Network** (ANN) **models** **using** the Levenberg−Marquardt (LM) training algorithm, with **global** **solar** **radiation** as the objective function. In doing this, ANN **models** with different numbers of neurons (from 5 to 250) in the hidden layer as well as different numbers of delays were experimented with, and their effect on **prediction** accuracy was analyzed. Subsequently the most accurate ANN model was used for **global** **solar** **radiation** **prediction** in ten cities across **New** **Zealand**. The predicted values of hourly **global** **solar** **radiation** were compared with the measured values, and it was found that the mean squared error (MSE) and regression (R) values showed close correlation. As such, the study illustrates the capability of the model to forecast **radiation** values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.

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In recent years, much research has been carried out on the application of ANN techniques to the load forecasting problem. As such, expert systems have been tried out (Ho et al., 1990), (Rahman and Hazim, 1993), and compared to traditional methods (Moghram and Rahman, 1989). The advantage of **using** ANN as compared to the other **models** is the ability to extract the implicit non- linear relationships among the variables by means of ‘‘learning’’ with training data. Many interesting ANN applications have been reported in power system areas, due to their computational speed, their ability to handle complex non-linear functions, robustness and great efficiency, even in cases where full information for the studied problem is absent. Thus the works of (Khotanzad et al., 1997), (Khotanzad et al., 1998) are good examples. Also it appears that the use of ANN for load forecasting has been well accepted in practice, and is used by many utilities (Khotanzad et al., 1998).

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intelligence (AI), which belongs to the group of computational algorithms called connectionist **models** [11]. ANN modeling technique offers a better solution for developing a more generalized model for **prediction** of **solar** **radiation** data **using** climatological parameters. It is a modeling and **prediction** tool, widely accepted as a technique offering an alternative way to tackle complex and ill-defined problems [9].

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Given that there is evidence that **prediction** **models** are sensitive to time period and distress situations other than those originally developed for (Perez 2006), the conceptualize model allows for flexibility of data input and a wider selection of ratios to improve insolvency **prediction** or enhancing precision in the coefficient estimates (MDA) of a failed company as demanded for specific situations. It is not suggested, however, that managers should focus solely on the results of financial ratios when making decisions on the viability of a company. Managers should also consider macroeconomic variables that are known to influence corporate insolvency. These macroeconomic variables can serve as input to the knowledge base of the **neural** **network** systems to improve their predictive power and include the rate of inflation, the annual growth rate in real GDP and the unemployment rate. In addition, strict corporate governance systems and strict conformation statutory reporting should be in place. Effective corporate governance systems foster transparency and accountability by ensuring their shareholders receive quality information about the company's performance and the directors' stewardship of their assets. This ensures that shareholders are able to exercise their powers to hold directors to account.

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Archana Nair et al. [9] applied a nonlinear technique i.e., **Artificial** **Neural** **Network**. Authors has been developed **Global** Climate **Models** (GCMs). In this study, GCM are considered from the National Centre for Environmental **Prediction** (NCEP) and the International Research Institute (IRI). The monthly and seasonal rainfall information had been predicted over the Indian domain of different tropical region. Nowadays, the scientist of the meteorological community are being faced many challenges in the rainfall **prediction** concerned. In this study, two types of dataset are required: GCM predicted hindcast dataset, these type of dataset are collected from observational dataset and International Research Institute (IRI), these type of dataset are collected from Indian Meteorological Department (IMD). This analysis is developed by **using** double cross-validation and simple-randomization technique on dataset. The performance of coupled and uncoupled are enhanced the **prediction** of rainfall of the individual months **using** the ANN technique.

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In addition, Behrang et al. have compared the performance of two types of **neural** networks-Multilayer Perceptron (MLP) and Radial Basis Function (RBF)-in order to predict the amount of daily **global** **solar** **radiation** of Dezful city in Iran. **Using** average daily temperature inputs, relative humidity, sunshine hours, evaporation rate and wind speed, they have come into result that multilayer **network** with Mean Absolute Percentage Error of 5.21% and Absolute Fraction of Variance of 99.57% produces a better result compared with Radial Basis Function (RBF) [15]. Benghanem and Mellit have predicted the daily **global** **solar** **radiation** with correlation coefficient of 98.8% **using** Radial Basis Function (RBF) **neural** **network** and also some input parameters such as air temperature, the sunshine duration and relative humidity [16].

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Renewable energy sources derive enormous energy from the sun’s **radiation**. **Global** **Solar** **Radiation** **prediction** is essential in Photo Voltaic power plants for efficient sizing and improving the performance of these systems. Some computational Intelligence methods are used in time series d on the statistical data. A number of **neural** **network** **models** like Radial Basis function (RBF) and Multilayer perception (MLP) were used and these are all forward **prediction** methods which may result in inaccuracy of **prediction**. Here, Recurrent **Neural** **Network** (RNN), in which a feedback from the output layer is given as input to one of the hidden layers has been used. Input variables used for **prediction** are Day of the month, daily mean air temperature, N is being trained **using** Particle Swarm Optimization (PSO) and Evolutionary algorithm (EA).EA is stochastic search and optimization heuristics derived from evolutionary theory.PSO is an optimization based technique used for solving mensional problems. Also, performance of these algorithms is compared by

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This paper proposes an alternative to the use of empirical **models**, which is the utilization of a combination of pattern recognition through data clustering tech- niques [3] as well as data modelling through **artificial** **neural** networks [4]. Through the clustering of data, an organized set of inputs can be used to more ac- curately train the **neural** **network** for use in predicting data. A focused time-delay **neural** **network** [5] is then used on each cluster.

All the input parameters feed their respective values to the input layer which processes that data towards hidden layer. All the functioning (training, validation and testing) of developed **neural** **network** takes place in the hidden layer of the **network**. The estimated **solar** **radiation** value for the respective location gets reflected at output layer. The measured data (1962-1978) of eleven parameters (mean value of year, duration, month, latitude, longitude, altitude, sun shine hours, temperature, humidity, wind speed and rain fall) given by Mani and Rangarajan for eighteen locations (Ahmedabad, Bangalore, Bhavnagar, Mumbai, Kolkatta, Goa, Jodhpur, Kadaikanal, Chennai, Mangalore, Nagpur, Nandi Hills, **New** Delhi, Poona, Port Blair, Shillong, Thiruvanathapuram and Vishakhapatnam) have been taken to train, validate and test the developed model (Mani and Rangarajan, 1980). In order to train the developed **artificial** **neural** **network** model for the estimation of **global** **solar** **radiation** 70 % of the measured data has been used while 15 % data has been used for the purpose of validation and the remaining 15 % data has been used for the testing purpose. A graphical user interface for **solar** **radiation** estimation with MATLAB programming has been framed and developed software has been checked for valid results. All the data has been linked with the developed graphical user interface to give the output for the desired inputs. The developed graphical user interface can be used to estimate **global** **solar** **radiation** at any location in India.

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Adnan Sozen et al (2004) estimated the **solar**-energy potential in Turkey **using** **artificial** **neural**-**network** (ANN). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (PRCG), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the **network**. In order to train the **neural** **network**, meteorological data’s was taken from eleven stations spread over Turkey and six stations data are used for testing. Meteorological and geographical data like latitude, longitude, altitude, month, mean sunshine duration, and mean temperature are used as inputs to the **network**. **Solar** **radiation** is the output layer. The trained and tested ANN **models** show greater accuracies for evaluating **solar** resource possibilities in regions where a **network** of monitoring stations has not been established in Turkey. The predicted **solar**-potential values from the ANN were given in the form of monthly maps. These maps are of prime importance for different working disciplines, like those of scientists, architects, meteorologists, and **solar** engineers in Turkey.

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The main objective of this paper is to employ the **artificial** **neural** **network** (ANN) **models** for validating and predicting **global** **solar** **radiation** (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN **models**. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.

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ANN mimics the function of human brain. Since its inception, ANN has become popular and applicable to various fields of science and technology to solve complicated simulation problems. The ANN is capable of calculating arithmetic and logical functions, generalizing and transforming independent variables to the dependent variables, parallel computations, nonlinearity processing, handling noisy data, function approximation and pattern recognition [3]. ANN is trained **using** a set of real inputs and their corresponding outputs. For a better approximation, sufficient number of datasets is required. Performance of the trained model is checked with part of the available data known as testing datasets. To find out the best possible **network**, various topologies are constructed and tested. The process of model training- testing has to be continued until the optimum model with minimum error and maximum accuracy is achieved. A **neural** **network** has a layered structure, and each layer contains processing units or neurons. Input variables are placed in the input layer, whereas target variables are put in the output layer. The neurons in the hidden layers are the intermediate computation components (black box) of

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Abstract: Predicting wire length before placement is called priori wire length estimation. Here we propose a **neural** **network** based approach to estimate the total wire length of the digital circuit. Here a three layer **neural** **network** is used; a **neural** **network** quickly learns the behavior of the placement tool and gives the result similar to the placement tool. Techniques to estimate wire length are becoming important because we need to optimize the circuit in terms of power dissipation, speed and delay when one has freedom to do so. The simulation tools used here are Xilinx and matlab .Xilinx was used to extract the circuit parameters and matlab was used to create a 3 layer **neural** **network**.

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neuro-fuzzy inference system (ANFIS) have been applied to model a wide range of challenging problems in science and engineer- ing. ANN displays better performance in bankruptcy **prediction** than conventional statistical methods such as discriminant analysis and logistic regression ( Quah & Srinivasan 1999 ). Investigations in credit rating process showed that ANN has better **prediction** ability than statistical methods due to complex relation between ﬁnan- cial and other input variables ( Hájek, 2011 ). Bankruptcy **prediction** ( Alfaro, García, Gámez, & Elizondo, 2008; Lee, Booth, & Alam, 2005; Baek & Cho, 2003 ), credit risk assessment ( Yu, Wang, & Lai, 2008; Angelini, Di Tollo, & Roli, 2008 ), and security market applications http://dx.doi.org/10.1016/j.jefas.2016.07.002

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Recently, research endeavours in the area of online voltage stability assessment **using** machine learning approaches have received increasing research interest due to their broad range of applications and ease. In the field of voltage stability analysis, numerous researches **using** machine learning is ongoing with **artificial** **neural** **network** (ANN), fuzzy logic **network**, support vector machine, decision trees, and neuro- fuzzy networks, as seen in [9-16]. ANN includes many components; these components are the single-input neuron or the Multiple-Input Neuron, as shown in Figure 1 [17]. This machine learning-based voltage collapse **prediction** methods address the shortcomings of the conventional techniques aforementioned.

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Bhanja and Sengupta (2005) worked on Influence of silica fume on the tensile strength of concrete. Extensive experimentation was carried out over water–binder ratios ranging from 0.26 to 0.42 and silica fume–binder ratios from 0.0 to 0.3. For all the mixes, compressive, flexural and split tensile strengths were determined at 28 days.A. Elahi et al, carried out investigation to evaluate the mechanical and durability properties of High Performance Concrete (W/B = 0.3) containing supplementary cementitious materials (Silica Fume, Fly Ash, Ground Granulated Blast Furnace Slag) in binary and ternary systems. Portland cement was replaced with fly ash upto 40%, silica fume upto 15% and GGBS upto a level of 70%. The ternary mixes containing GGBS or Fly Ash (50%) and Silica Fume (7.5%) performed the best amongst all the mixes to resist the chloride diffusion. Silica fume (7.5%) performs better than other supplementary cementitious materials for the strength development.B. K. Raghu Prasad et al, proposed an **artificial** **neural** **network** (ANN) to predict 28 days compressive strength of high performance concrete. The high values of R 2 demonstrated

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The **prediction** learning method implemented is an LSTM (Long Short Term Memory) recurrent **neural** **network**. We have assumed that a recurrent **neural** **network** is capable of capturing time-dependent trends in the data because feedback loops enable RNN’s to exhibit memorization of temporal behaviour. Developing the Recurrent **Neural** **Network** involved sampling performance on the basis of a wide range of modifiable parameters which includes the size and number of hidden layers, types of activation functions, type of optimization and regularization, batch and epoch sizes, and cross-validation methods.

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Concrete cube strength determination tests are usually performed at three days to one year after pouring the concrete. The waiting period required to perform such test may delay the construction progress, decision making and neglecting such test would limit the quality control checks in large construction projects. Therefore it becomes necessary that the rapid and reliable **prediction** of concrete strength is essential for pre-design or quality control of construction. It is possible to facilitate the modification of the mix proportion if the concrete does not meet the required design stage, which may save time and construction costs. The early **prediction** of concrete strength is essential for estimating the desirable time for concrete form removal, project scheduling, quality control and estimating delay if any. **Artificial** **Neural** **Network** (ANN) is used to predict the compressive strength of concrete. Standard back propagation and Jordan–Elman algorithms are used to train the networks. Networks are trained and tested at various learning rate and momentum factor and after many trials these were kept constant for this study. Performance of networks were checked with statistical error criteria of correlation coefficient, root mean squared error and mean absolute error. It is observed that **artificial** **neural** networks can predict compressive strength of concrete with 91 to 98 % accuracy.

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Abstract: The objective of this study is to apply **artificial** **neural** **network** (ANN) for development of bus travel time **prediction** model. The bus travel time **prediction** model was developed to give real time bus arrival information to the passenger and transit agencies for applying proactive strategies. For development of ANN model, dwell time, delays and distance between the bus stops was taken as input data. Arrivals/departure times, delays, average speed between the bus stop and distance between the bus stops were collected for two urban routes in Delhi. Model was developed, validated and tested **using** GPS (**Global** Positioning System) data collected from field study. Comparative study reveals that ANN model outperformed the regression model in terms of accuracy and robustness.

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