Top PDF Unit Trust Forecasting using Adaptive Neural Fuzzy Inference System: A Performance Comparison

Unit Trust Forecasting using Adaptive Neural Fuzzy Inference System: A Performance Comparison

Unit Trust Forecasting using Adaptive Neural Fuzzy Inference System: A Performance Comparison

Unit trust market is equally important as stock market as both are contributed significantly to nation’s economic performance. Success in investing unit trust may also promises attractive benefits for investors. However, tasks to ensure successful prediction are highly complicated as many uncertainty and unpredictable factors involved. In this paper, the forecast ability of Net Asset Value (NAV) of three unit trust funds with Adaptive Neural Fuzzy Inference System (ANFIS) is examined. The objective of this study is to forecast NAV of three unit trust funds using ANFIS. Three unit trust funds were selected to model and forecast the NAV. One by four of input structure for each unit trust was defined prior to determining fuzzy rules in the fuzzy forecast. The experimental results indicate that the model successfully forecasts the NAV of the unit trust funds. The forecasting errors for the three funds were in the ranges of [-0.2461, 0.1], [-0.1384,0.08], and [-0.025,0.015]. The Pru Bond Fund recorded the least errors among the three funds. ANFIS offers a promising tool for economists and market players in dealing with forecasting NAV of unit trusts.
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Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract — The aim of the present study is to explore applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood for the case study, Dharoi Dam on the Sabarmati river near village Dharoi in Kheralu Taluka of Mehsana District in Gujarat State, India. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R 2 ) and Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. This objective is accomplished by evaluating the model by comparing ANFIS model to Statistical method like Log Pearson type-III method to forecasting flood. This comparison shows that ANFIS model can accurately and reliably be used to forecast flood in this study.
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Application of Adaptive Neural Fuzzy Inference System for the Prediction of Software Defects

Application of Adaptive Neural Fuzzy Inference System for the Prediction of Software Defects

We implemented Adaptive Neuro Fuzzy Inference device for software illness prediction. In MAT lab, there may be a toolbox Neuro Fuzzy device field which implements ANFIS. In the information section, education information turned into loaded. In FIS section, preliminary Sugeno Fuzzy Inference system was derived with the aid of using subtractive clustering method. Subtractive clustering approach takes 4 parameters range of impact (0.3), Squash thing (1.25) accept ratio (0.5) & Reject Ratio (0.15). In training segment, FIS became educated the usage of ANFIS set of rules. In trying out segment, FIS became tested with unseen records. The Receiver Operating Traits (ROC) was plotted in opposition to true fine charge with false wonderful price. AuC values are decided for every dataset of software illness prediction and consequences are in comparison with price sensitive neural networks. The performance of the classifier is measured in terms of location beneath ROC Curve (AuC) values
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Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System

Forecasting of Intellectual Capital by Measuring Innovation Using Adaptive Neuro-Fuzzy Inference System

– Today most organizations have discovered that advanced trainings can be considered as the key asset for modern organizations. This study presents a forecasting model that predicts intangible assets on the basis of innovation performance in organizational training using widely applied innovation criteria. The research focused on criteria, such as organization culture, ability to respond to organizational technology changes, relationship with other organizations in the training process and the use of high technology in education. The adaptive neuro-fuzzy inference systems (ANFIS) approach has been used to verify the proposed model. It is possible to predict innovation performance and it can also adjust allocated resources to organizational training system for its innovation objectives with this method. Originality/value – A great deal of work has been published over the past decade on the application of neural networks in diverse fields. This paper presents a model that measure and forecasts the intangible assets by the development of an Adaptive Neural Network with Fuzzy Inference system (ANFIS), using data that concern human capital, organizational support and innovativeness. The results indicate that fuzzy neural networks could be an efficient system that is easy to apply in order to accurately measure and forecast the intangible assets by measuring innovation capabilities of an organization or firm.
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Medium Term Load Forecasting using Adaptive Neuro Fuzzy Inference System

Medium Term Load Forecasting using Adaptive Neuro Fuzzy Inference System

Load forecasting plays important tasks in power system planning, operation and control. It has received an increasing attention over the years by academic researchers and practitioners. Control, security assessment, optimum planning of power production required a precise medium term load forecasting. Electric load forecasting is a real-life problem in industry. Electricity supplier’s use forecasting models to predict the load demand of their customers to increase/decrease the power generated and to minimize the operating costs of producing electricity. In addition to the conventional classical models, several models based on artificial intelligence have been proposed in the literature, in particular, neural network for their good performance. Other nonparametric approaches of artificial intelligence have also been applied. Nevertheless, all these models are inaccurate when used in real time operation. This paper presents the novel techniques of using Adaptive Neuro Fuzzy Interference System for prediction of hourly load power system data which combines neural network and fuzzy logic to predict future load. ANFIS model is constructed using one complete year load data from TATA POWER Company, Mumbai, applying Genfis 2 and Genfis 3 to train & test the data. The RMSE, MAPE & SD are used as Performance indices to evaluate the model.
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Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques

Reservoir Inflow Forecasting Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Techniques

The simulation results from the selected ANFIS and ANN models during training, validation and testing revealed the superiority of the ANN model. The selected ANFIS model gives lower values in most of the performance indices during training. For validation and testing, all performance indices of selected ANFIS model were inferior to those of the ANN model. The weakness of ANFIS model is shown in its inability to forecast individual peak flows. The sudden flow changes in these small tropical catchments resulting in these peak flows are common due to their small areal extent and to the intense localized phenomenon of tropical showers.
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Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

Weather Forecasting Models Using Neural Networks and Adaptive Neuro Fuzzy Inference for Two Case Studies at Huoston, Texas and Dallas States

produced time series analysis technique for improving the real time flood forecast by a deterministic lumped rainfall runoff model and they concluded that apart from ANNs with adaptive training , all the time series analysis techniques considered allow significant improvements if flood forecasting accuracy compared with the use of empirical rainfall predictors Brath, et al. [2]. Iseri, et al. [3] have developed medium term forecasting of August rainfall in Fukuoka city. In order to identify the sufficient predictors, the partial mutual information was used for the candidate predictors, which are Sea Surface Temperature anomalies (SSTa) in the Pacific Ocean and three climate indices. When data with lead times between one and twelve months were used to forecast August rainfall, it was found that a model with the North Pacific index and selected SSTa as inputs performed reasonably well. Iseri, et al. [3]. Nayaka, et al. [4] have applied an adaptive neuro-fuzzy inference system (ANFIS) to hydrologic time series modeling, and it was concluded that the ANFIS model preserves the potential of the ANN approach fully, and eases the model building process Nayaka, et al. [4]. KISI [5] used three different neural network (NN) architectures, i.e. ANN, Auto-Regressive Models and sum of square errors, for comparison of forecasting probabilities and it was found in this study that ANNs were able to produce better performance than AR models when given the same data inputs KISI [5]. Ramirez, et al. [6] also used a Multi Layer Feed-forward Perceptron (MLFP) neural network for daily precipitation prediction in the region of Sao Paolo State, Brazil. The potential temperature, vertical component of the wind, specific humidity, air temperature, perceptible water, relative vorticity and moisture divergence flux were used as input data for training of networks. The results s of ANN were
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Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

Rainfall Forecasting in Banyuwangi Using Adaptive Neuro Fuzzy Inference System

Predicting the rainfall is a complex process due to the incidence of rain is non linear and dynamic. Many studies have tried to modeling rainfall forecasting to predict accurately. Such as statistical model approach like Multiple Linear Regression and artificial intelligence approaches like Fuzzy Inference System [8], Artificial Neural Networks (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS). Comparison of these approaches show ANFIS and GA predict more accurate than other methods. And ANFIS that use hybrid training method give better results [9]. Another study shows that ANFIS is better than ANN in forecasting rainfall monthly [10]. Since the popularity of ANFIS in predicting rainfall, further investigation is needed in forecasting rainfall using ANFIS.
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Comparison between Neural Network and Adaptive Neuro Fuzzy Inference System for Forecasting Chaotic Traffic Volumes

Comparison between Neural Network and Adaptive Neuro Fuzzy Inference System for Forecasting Chaotic Traffic Volumes

on past values [9], the polynomial model [10], neural network-based black-box models [11-15], a model con- sisting of a fuzzy C-means clustering and a radial-ba- sis-function neural network [16], etc. This paper also tries to forecast the short-term chaotic traffic volume at the intersection. Two kinds of models are presented for comparison. One is the neural network, where the delay coordinates [2,17,18] of the reconstructed state space of the traffic flow system are used as the input vector of the neural network and the first delay coordinate of next state as the target of the neural network. The other model is the adaptive neuro-fuzzy inference system [19,20], where inputs and targets are identical to the first one, but mem- bership functions and fuzzy rules [21,22] replace neurons in the neural network. The number and the shapes of the membership functions are decided and tuned by a data clustering technique and backpropagation neural network, respectively, which is different from the Park’s model [16] in the ways of data clustering and learning process.
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Short Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

Short Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

making it difficult to predict its future value, but with the proposed approach significant improvement in the fore- casting performance has been observed. One key element in the processing is selection of input data and careful preprocessing. Data clustering into ranges in which ob- servable trends are maintained allows for the extraction of such trends using separate ANN trained for each specific cluster. Data samples that cannot fit into the strictly de- fined clusters form intersection zones that, in addition to ANN, are processed using fuzzy inference.
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Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

Rainfall-runoff modelling using adaptive neuro-fuzzy inference system

In the set of data have two variables are used, water discharge (Q) and rainfall (R). In this modelling, it experiments the variables by set or group. Table 2 shows, there are four sets of data that have been modelled using ANFIS. After filtering the dataset, the data is ready to be loaded by ANFIS toolbox. The sets of data are as in Table 2.

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Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. Ac- cording 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 are 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 differ- ent sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has suf- ficient accuracy for apnea detection considering of each type of respiratory. However, the best result is 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 entire data.
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Diagnosis of Hepatitis using Adaptive Neuro Fuzzy Inference System (ANFIS)

Diagnosis of Hepatitis using Adaptive Neuro Fuzzy Inference System (ANFIS)

In this study, we have been able to develop an architecture model that combines the knowledge-base and reasoning features of fuzzy logic (FL), with self-learning capacity of artificial neural network (ANN) to diagnose Hepatitis B disease by computing the intensity levels of its attack. Medical field refers to diagnosis as an approach of recognizing a disease through the analysis of underlying physiological symptoms. Hepatitis is a chronic liver disease that can easily decompensate to cirrhosis disease if its severity is not properly checked with appropriate tools. This study can improve the quality of liver disease diagnosis and provide appropriate information for medical practitioners in the process of administering treatment. This work could be further expanded for improved optimal solutions by integrating Genetic Algorithm (GA) with ANFIS.
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Modeling environmental indicators for land leveling, using Artificial Neural Networks and Adaptive Neuron-Fuzzy Inference System

Modeling environmental indicators for land leveling, using Artificial Neural Networks and Adaptive Neuron-Fuzzy Inference System

ABSTRACT: Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines requires considerable amount of energy, it delivers a suitable surface slope with minimal soil deterioration as well as damage to plants and other organisms in the soil. Notwithstanding, in recent years researchers have tried to reduce fossil fuel consumption and its deleterious side effects, using new techniques such as Artificial Neural Networks (ANNs) and Adaptive Neuron- Fuzzy Inference System (Fuzzy shell-clustering algorithm) models that will lead to a noticeable improvement in the environment. The present research investigates the effects of various soil properties such as Embankment Volume, Soil Compressibility Factor, Specific Gravity, Moisture Content, Slope, Sand Percent, and Soil Swelling Index in energy consumption. The study consists of 90 samples, collected from three different regions. The grid size has been set on 20 m * 20 m from a farmland in Karaj Province, Iran. The aim is to determine the best linear model, using ANNs and ANFIS model to predict environmental indicators and find the best model for land leveling in terms of its output (i.e. Labor Energy, Fuel energy, Total Machinery Cost, and Total Machinery Energy). Results show that ANFIS can successfully predict labor energy, fuel energy, total machinery cost, and total machinery energy. All ANFIS-based models have R 2 values above 0.995 and MSE values below 0.002 with higher accuracy in prediction, given their higher R 2 value and lower RMSE value.
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Predicting the Collapsibility Potential of Unsaturated Soils Using Adaptive Neural Fuzzy Inference System and Particle Swarm Optimization

Predicting the Collapsibility Potential of Unsaturated Soils Using Adaptive Neural Fuzzy Inference System and Particle Swarm Optimization

collapsibility. One of the primary criteria was presented by Clevenger, who only considered the eects of dry unit weight [3]. The other criterion was considered by Gibbs and Bara, who employed dry unit weight and liquid limit to separate collapsible and non-collapsible soils [4]. Denisov investigated the ratio of soil natural porosity to soil porosity during liquid conditions as a controlling parameter [5]. Fookes and Best also used a collapsibility index that was the ratio of the dierence of natural and critical porosity to dierence of soil porosity in liquid and plastic limits [6]. The principles dominating saturated soil are adequately extended, both theoretically and practically, and the mechanical behavior of these soils is a function of an eective stress principle [7]; however, considering this principle for unsaturated soils has been only successful to a limited extent that requires more studies and attentions [8,9]. Since the collapsibility phenomenon occurs in these soils, the necessity of doing numerical researches is revealed in accordance with the unsat- urated soil mechanics theory. Attempts have been made in the late 1980s to provide mathematical and computer models to analyze unsaturated collapsible soil behaviors. For instance, Amirsoleimani (1988) presented a mathematical model with regard to soil stability and statistical principles. Moreover, Hay- dari (1990) analyzed the lateral compression eects on the behaviors of collapsible soil by presenting a mathematical model. DeBon et al. (1998) published the results of their researches regarding the behavioral model of collapsible soil by Monte Carlo analysis. Another method of modeling considered in the past few decades was the use of soft computing in civil engineering. For example, the work of Khademi et al. (2016) presented prediction result of concrete compressive strength using articial neural network modeling [10]; Zorlu and Gokceoglu (2008) dealt with predicting the collapsibility index using a double input fuzzy inference system in the Mamdani method [11]; the activities by Momeni et al. (2011) focused on predicting soil collapsibility potential using the fuzzy system [12]. Kang and Li published Articial bee colony algorithm optimized support vector regression for system reliability analysis of slopes [13]. The other work done in this regard was carried out by Basma and Tuncer, who presented formulas according to the data from laboratory test experiments and regression analysis [14]. The accuracy of these relations was evaluated by Habib-agahi and Taherian who utilized neural networks [15].
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Cuckoo search based adaptive neuro-fuzzy inference system for short-term load forecasting

Cuckoo search based adaptive neuro-fuzzy inference system for short-term load forecasting

ii. Presence of GD (a derivative based optimization technique) in ANFIS network is making the computation within the network to be complex, and has a tendency of being trapped in the local optima. There is need of replacing the GD in the ANFIS network with search-based optimization algorithm that has fewer parameters to be tuned, and has a very wide and deep search capability. iii. The big data problem associated with load forecasting is beyond just incorporating calendar variables in the model inputs. This is because the load profile has taken care of seasonal effect in the load series. The proper way to handle overlapping of one timeframe (season, week, day or hour) over the other is to formulate the forecasting in such a way that each season is treated separately.
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Survey of fuzzy inference model and impact on QOS parameters using 
		adaptive neuro fuzzy inference system in MANET

Survey of fuzzy inference model and impact on QOS parameters using adaptive neuro fuzzy inference system in MANET

Shafigh, A. S. Abdollahi , K. Kassler Andeas J [1] proposed Fuzzy logic control method to improve the performance and reliability of the multicast routing protocols in MANET. Strong and small forwarding group is established to decrease the resource consumption and higher stability of the delivery structure. A forwarding group is made out of set of strong /weak nodes. Fuzzy logic is proposed to distinguish the strong and weak nodes in the network. Join query packet is periodically broadcasted to update the routes in the network. An intermediate node receives a non-duplicate join query; it stores the upstream node ID into the routing table and rebroadcasts the packet. A node receives a join query message; it needs to fuzzyfys the parameters such as bandwidth, node speed and power level of previous node. The value of previous node's parameter is used to classify them as low, medium or high. After fuzzification, inference process is used to derive the probability of caching and forwarding the join query to other nodes. Using fuzzy based approach only links and nodes which are more robust or have more available power will participate in the forwarding mesh.
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Voltage Stability Monitoring Using Adaptive Neuro-Fuzzy Inference System

Voltage Stability Monitoring Using Adaptive Neuro-Fuzzy Inference System

results but they are usually hampere by the fact that they consume long computing time because of the requirement for repetitive power flow calculations. Online voltage security assessment is a very useful but not yet becomes a widely used tool that measures the distance from the current operating condition at any time to the critical point. ADAPTIVE NEURO FUZZY INFERENCE SYSTEMhave recently received widespread attention from researchers for this application. Most of ANFIS applications have been implemented using multi-layered feed-forward neural networks trained by back propagation because of their robustness to input and system noise, their capability of handling incomplete or corrupt input data. However, in typical power systems there are voluminous amount of input data. Then, the success of ANFIS applications also depends on the systematic approach of selecting highly important features which will result in a compact and efficient ANFIS. In this part, several voltage stability indicators are calculated. It should be mentioned here that this paper aims at implementing these already proposed indicators by ANFIS. The capability of monitoring proximity to voltage collapse was tested beforehand, but unfortunately due to space limitation and scope of this paper the complete results cANFISot be presented.
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Application of Artificial Neural Network and Adaptive Neural based Fuzzy Inference System Techniques in Estimating of Virtual Water

Application of Artificial Neural Network and Adaptive Neural based Fuzzy Inference System Techniques in Estimating of Virtual Water

Iran is located at an arid and semi-arid region with low precipitation and high evapotaranspiritation. Moreover the distribution of water resources availability is uneven spatially and temporally, so that the central, eastern and southern part of the country faces drought. Also in the absence of irrigation, rainfed agriculture has a high risk. The average annual precipitation is about 252 mm, but the average annual evaporation capacity is about 1500-2000mm. regarding the evaporation capacity, 71% of annual precipitation about 179mm vaporize directly. The regional actual water use is 87.5×10 9 m 3 , of which agricultural irrigation water accounting for 94.25%, and urban water 4.75 and industrial water use 1% (Alizadeh and Keshavarz, 2005). The specific climate condition led to unsuitable temporal and local precipitation hence, producing sustainable agricultural products which subject to the proper use of water resources. Growing population and urbanization have increased required water and food unexpectedly in the country. Considering the mentioned climate conditions and limitation of using new water resources, and on the other hand the necessity of increasing agricultural products, considering Virtual Water (VW) content of these products is vital to manage the water resources.
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A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting

A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting

The paper is organized as follows: section II describes an overview of the algorithm that has been used for the model’s training and the energy forecast while section III outlines a brief explanation about the Genetic Algorithm’s operation. In section IV, the proposed methodology is presented explaining the combination of the two algorithms in order to develop a system capable to train the consumption models autonomously. In Section V, the implementation of the system in the pilot plant is explained, presenting the different results that have been obtained during the test and the evaluation of the system. Finally, section VI summarizes the paper and discusses the different conclusions.
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