In the structure of ANFIS, there are two different parameter groups: premise and consequence. Training ANFIS means determination of these parameters using an optimization algorithm. In the first ANFIS model developed by Jang, a hybrid learning approach was proposed for training. In this approach, while premise parameters are determined by using gradient descent (GD), consequence parameters are found out with least squares estimation (LSE) method. Since ANFIS has been developed, it is used in modelling and identification of numerous systems and successful results have been achieved. The selection of optimization method utilized in training is very important to get effective results with ANFIS. It is seen that derivate based (GD, LSE etc.) and non-derivative based (heuristic algorithms such us GA, PSO, ABC etc.) algorithms are used in ANFIS training. Nevertheless, it has been observed that there is a trend toward heuristic based ANFIS training algorithms for better performance recently. At the same time, it seems to be proposed in derivative and heuristic based hybrid algorithms. Within the scope of this study, the heuristic and hybrid approaches utilized in ANFIS training are examined in order to guide researchers in their study. In addition, the final status in ANFIS training is evaluated and it is aimed to shed light on further studies related to ANFIS training.

Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Despite the extensive use of mortars materials in constructions over the last decades, there is not yet a reliable and robust method, available in the literature, which can estimate its strength based on its mix parameters. This limitation is due to the highly nonlinear relation between the mortar’s compressive strength and the mixed components. In this paper, the application of artificial intelligence techniques toward the prediction of the compressive strength of cement-based mortar materials with or without metakaolin has been investigated. Specifically, surrogate models (such as artificial neural network, ANN and adaptive neuro-fuzzy inference system, ANFIS models) have been developed to the prediction of the compressive strength of mortars trained using experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of both ANN and ANFIS models to approximate the compressive strength of mortars in a reliable and robust manner. Although ANFIS was able to obtain higher performance prediction to estimate the compressive strength of mortars compared to ANN model, it was found through the verification process of some other additional data, the ANFIS model has overfitted the data. Therefore, the developed ANN model has been introduced as the best predictive technique for solving problem of the compressive strength of mortars. Furthermore, using the optimum developed model an ambitious attempt to reveal the nature of mortar materials has been made.

A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength

/pdf/how-the-mobile-communication-5b8eflx7w5.pdf

How the mobile communication

QoS in IEEE 802.11-based wireless networks: A contemporary review

Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.

/pdf/anfis-adaptive-neuro-fuzzy-inference-system-a-survey-19mm6rjdtu.pdf

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

We successfully demonstrate 40 GB/s 8 channels’ Dense Wavelength Division Multiplexing (DWDM) over free space optical (FSO) communication system. Each channel is transmitting 5 GB/s data rate in downstream separated by 0.8 nm (100 GHz) channel spacing with 1.8 GHz filter bandwidth. DWDM over FSO communication system is very effective in providing high data rate transmission with very low bit error rate (BER). The maximum reach of designed system is 4000 m without any compensation scheme. The simulation work reports minimum BER for Return-to-Zero (RZ) modulation format at different channels 1, 4, and 8 are found to be , , and at 4000 m distance, respectively. Sharp increase in BER occurs if data rate and distance increase up to 10 GB/s and 5000 m.

/pdf/performance-investigation-of-40-gb-s-dwdm-over-free-space-2ckewfjjk1.pdf

Performance Investigation of 40 GB/s DWDM over Free Space Optical Communication System Using RZ Modulation Format

Performance evaluation of reactive routing protocols in MANET networks using GSM based voice traffic applications

Mobile Adhoc Networks (MANETs) are the self-organized networks in which connections are not established for exchange the information. In MANET, there are major problems like scalability, dynamic topology, high mobility and routing. The network can be damaged due to the high mobility. Topology based routing can fail when there is a dynamic change in the network topology. To avoid these problems, Geographic routing is used. The geographic routing protocols are more efficient and scalable when there is a dynamic change in the network topology and when the mobility is high. In this paper, we have surveyed on the Hybrid Routing and Geographic routing protocol. The hybrid routing can be done into two ways i.e. greedy routing and face-2 algorithm or perimeter routing.

http://www.sersc.org/journals/IJGDC/vol9_no2/21.pdf

Geographic Routing Protocol: A Review

In a way to deal with real heath care problems, we proposed a fuzzy based decision- making system for diagnosis of breast cancer. Breast cancer is the most far- reaching disease today, so primary detection of Breast Cancer is very significant. The proposed paper was provided with artificial intelligent techniques such as fuzzy logic to give correct decision making. The fuzzy rule- based makes use of expert knowledge to deal with patient's symptom and give an accurate decision according to rules constructed.

Harsukhpreet Singh

Papers

ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

Performance Investigation of 40 GB/s DWDM over Free Space Optical Communication System Using RZ Modulation Format

Performance evaluation of reactive routing protocols in MANET networks using GSM based voice traffic applications

Geographic Routing Protocol: A Review

Fuzzy expert system for diagnosis of Breast Cancer