Inputs Of Adaptive Neuro-fuzzy Inference System Research Articles

Data-Driven AI-Based Parameters Tuning Using Grid Partition Algorithm for Predicting Climatic Effect on Epidemic Diseases

Adaptive Neuro-fuzzy Inference System (ANFIS) remains one of the promising AI techniques to handle data over-fitting and as well, improves generalization. Presently, many ANFIS optimization techniques have been synergized and found effective at some points through trial and error procedures. In this work, we tune ANFIS using Grid partition algorithm to handle unseen data effectively with fast convergence. This model is initialized using a careful selection of effective parameters that discriminate climate conditions; minimum temperature, maximum temperature, average temperature, wind speed and relative humidity. These parameters are used as inputs for ANFIS, whereas confirmed cases of COVID-19 is chosen as dependent values for two consecutive months and first ten days of December for new COVID-19 confirmed cases according to the Department of disease control (DDC) Thailand. The proposed ANFIS model provides outstanding achievement to predict confirmed cases of COVID-19 with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.99. Furthermore, data set trend analysis is done to compare fluctuations of daily climatic parameters, to satisfy our proposition, and illustrates the serious effect of these parameters on COVID-19 epidemic virus spread.

A Novel Technique for Maximum Power Point Tracking of a Photovoltaic Based on Sensing of Array Current Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract Maximum Power Point Tracking (MPPT) is now widely used method in increasing the photovoltaic (PV) efficiency. The conventional MPPT methods have many problems concerning the accuracy, flexibility and efficiency. The MPP depends on the PV temperature and solar irradiation that randomly varied. In this paper an artificial intelligence based controller is presented through implementing of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to obtain maximum power from PV. The ANFIS inputs are the temperature and cell current, and the output is optimal voltage at maximum power. During operation the trained ANFIS senses the PV current using suitable sensor and also senses the temperature to determine the optimal operating voltage that corresponds to the current at MPP. This voltage is used to control the boost converter duty cycle. The MATLAB simulation results shows the effectiveness of the ANFIS with sensing the PV current in obtaining the MPPT from the PV.

Discrimination of quarry blasts and microearthquakes using adaptive neuro-fuzzy inference systems in the Tehran region

The purpose of this research is to demonstrate the use of Adaptive Neuro-Fuzzy Inference System (ANFIS) for discrimination between quarry blasts and microearthquakes in the Tehran region using data from the Broadband Iranian National Network Center (BIN). In the south and southeast of Tehran, a large number of quarry blasts contaminate the earthquake catalog. In order to identify the real seismicity (tectonic earthquakes) in the region, we need to discriminate quarry blasts from natural earthquakes in the catalog. ANFIS classifiers were used for identifying and categorizing the two kinds of seismic events. Neuro-fuzzy coding was applied using six features as ANFIS inputs. We investigated waveforms of 506 seismic events (0.9<ML<4.2) from the BIN network and determined differences between earthquakes and quarry blasts based on input features. These features include the original time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and two features for spectral analysis of their seismograms. The results of this study indicate that, out of a total of 506 seismic records, the neuro-fuzzy ANFIS approach identified 24.5% (124 events) and 75.5% (386 events) were quarry blasts and natural earthquakes, respectively. Our revised earthquake catalog provides improved data for realistic earthquake hazard assessment. Moreover, active faults will be detected correctly.

Abstract WMP60: Automatic Embolic Signal Detection Using Adaptive Wavelet Packet Transform and Adaptive Neuro-Fuzzy Inference System

Introduction: Transcranial Doppler(TCD) can be used to detect emboli in cerebral circulation. To classify the measured TCD as an embolic signal (ES) or artifact is usually done by a well-trained physician. However, inter-rater reliability among those physicians is variable. Also,in countries where skilled physicians are scarce, an automatic ES detection system can be useful as a medical support system. Method: We propose a two-step algorithm based on adaptive Wavelet Packet Transform (WPT) and Adaptive Neuro-Fuzzy Inference System (ANFIS) described as follows. First, the TCD signal is windowed using a 256-sample Gaussian window with 80% overlap. A 3-level WPT is used to transform each windowed TCD signal. The best basis algorithm is applied to find the best binary tree from which the entropy and normalized energy are calculated. The entropy and normalized energy are used as features to classify each windowed TCD signal as normal or abnormal. Second, the abnormal TCD windows are classified further as ES or artifact. Specifically, standard deviation of level 3 WPT coefficients from frequency index 1 to 6 are calculated and used as inputs of ANFIS where each standard deviation can be combined with others resulting in 64 rules. After training the ANFIS, ES and artifact can be differentiated. Results: Six hundred and sixty abnormal signals in vivo are used to evaluate the algorithm, i.e., 176 ESs collected during emboli monitoring from patients undergoing carotid angioplasty with stenting and from patients with patent foramen ovale, 106 artifacts from normal subjects, and 378 artifacts from stroke patients with carotid stenosis. The signals are divided into training and validation sets. The training set is used to train the algorithm and the validation set is used to evaluate the validity of the algorithm. Experimental results show that the algorithm can differentiate abnormal from normal signals efficiently with 100% accuracy. A sensitivity of 96.6% and specificity of 96.5% can be achieved from the validation set. Conclusions: The automatic embolic signal detection algorithm has been developed. The experimental results suggested that the algorithm could detect emboli and differentiate artifacts efficiently and could be used as a medical support system.

Application of ANFIS to predict crop yield based on different energy inputs

In this paper, adaptive neuro-fuzzy inference system (ANFIS) was used to predict the grain yield of irrigated wheat in Abyek town of Ghazvin province, Iran. Due to large number of inputs (eight inputs) for ANFIS, the input vector was clustered into two groups and two networks were trained. Inputs for ANFIS 1 were diesel fuel, fertilizer and electricity energies and for ANFIS 2 were human labor, machinery, chemicals, water for irrigation and seed energies. The RMSE and R2 values were found 0.013 and 0.996 for ANFIS 1 and 0.018 and 0.992 for ANFIS 2, respectively. These results showed that ANFIS 1 and ANFIS 2 could well predict the yield. Finally, the predicted values of the two networks were used as inputs to the third ANFIS. The results indicated that the energy inputs in ANFIS 1 have a greater impact on the final yield production than other energy inputs. Also, the RMSE and R2 values for ANFIS 3 were 0.013 and 0.996, respectively. These results showed that ANFIS 1 and the combined network (ANFIS 3) could both predict the grain yield with good accuracy.

Adaptive neuro-fuzzy inference systems for semi-automatic discrimination between seismic events: a study in Tehran region

Thisarticle presents an adaptive neuro-fuzzy inference system (ANFIS) for classification of low magnitude seismic events reported in Iran by the network of Tehran Disaster Mitigation and Management Organization (TDMMO). ANFIS classifiers were used to detect seismic events using six inputs that defined the seismic events. Neuro-fuzzy coding was applied using the six extracted features as ANFIS inputs. Two types of events were defined: weak earthquakes and mining blasts. The data comprised 748 events (6289 signals) ranging from magnitude 1.1 to 4.6 recorded at 13 seismic stations between 2004 and 2009. We surveyed that there are almost 223 earthquakes with M ≤ 2.2 included in this database. Data sets from the south, east, and southeast of the city of Tehran were used to evaluate the best short period seismic discriminants, and features as inputs such as origin time of event, distance (source to station), latitude of epicenter, longitude of epicenter, magnitude, and spectral analysis (fc of the Pg wave) were used, increasing the rate of correct classification and decreasing the confusion rate between weak earthquakes and quarry blasts. The performance of the ANFIS model was evaluated for training and classification accuracy. The results confirmed that the proposed ANFIS model has good potential for determining seismic events.

Adaptive neuro-fuzzy inference system for generation of diffuser dot patterns in light guides

We present an adaptive neuro-fuzzy inference system (ANFIS) strategy to generate dot patterns in a liquid-crystal display light guide panel. The ANFIS model combines the learning capabilities of neural networks and the knowledge illustration of fuzzy logic systems using linguistic expressions. A hybrid learning algorithm, based on the least square method and the back propagation algorithm, is utilized to identify the parameters of ANFIS. Two inputs of ANFIS are the dot radius and the distance from dots to a light source, and one output is the illuminance over a light guide panel. During the process of generating diffuser dot patterns, ANFIS carries out efficient input selection, rule creation, networks training, and parameter estimation to create an appropriate model by the learning algorithm. The results show that the proposed model can achieve an even illuminance condition and effectively improve brightness in accordance with the light source position. Moreover, a comparative analysis suggests that the ANFIS-based approach outperforms the traditional model in terms of overall illuminance and color uniformity.

Modeling and prediction of activity anti-HIV molecules using soft computing techniques

Several works quantitative structure-activity relationships (QSAR) of anti-Human Immunodeficiency Virus (HIV) molecules were studied by different statistical methods and non-linear models. But few studies have used the heuristic methods. In this paper, a hybrid decision trees (DT) and adaptive neuro-fuzzy inference system (ANFIS) is used of the prediction of inhibitory activity of anti-VIH molecules. DT algorithm is utilized to select the most important variables in QSAR modeling and then these variables were used as inputs of ANFIS to predict the anti-HIV activity. The model's predictions were compared with other methods and the results indicated that the proposed models in this work is superior over the others.