Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces

Utilizing the rainfall intensity, and slope data, a fuzzy logic algorithm was developed to estimate sediment loads from bare soil surfaces. Considering slope and rainfall as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relations among rainfall intensity, slope, and sediment transport were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF-THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The sediment load predicted by the fuzzy model was in satisfactory agreement with the measured sediment load data. Predicting the mean sediment loads from experimental runs, the performance of the fuzzy model was compared with that of the artificial neural networks (ANNs) and the physics-based models. The results of showed revealed that the fuzzy model performed better under very high rainfall intensities over different slopes and over very steep slopes under different rainfall intensities. This is closely related to the selection of the shape and frequency of the fuzzy membership functions in the fuzzy model

EVALUATION OF STUDENT ACADEMIC PERFORMANCE USING ADAPTIVE NEURO-FUZZY APPROACH

The Fusion of Artificial Neural Networks (ANN) and Fuzzy Inference System (FIS) has attracted a growing interest of researchers in various scientific and engineering areas. Due to the growing need for adaptive intelligent systems to solve real world problems. ANN learns by adjusting the interconnections between layers. FIS is a popular computing framework based on the concept of fuzzy set theory and fuzzy if-then rules. The advantages of the combination of ANN and FIS are apparent. The developed method uses a fuzzy system to support neural networks to enhance some of its characteristics like flexibility, speed and adaptability which is called the Adaptive Neuro-fuzzy inference system (ANFIS). Evaluating and assessing the student academic performance is not an easy task, especially when it involves many attributes or factors. Moreover, the knowledge of the human experts is acquired to determine the criteria of students’ academic performance and the decisions about their level of assimilation but most of the information is incomplete and vague. To overcome the problem, this work evaluates the student’s academic performance based on ANFIS tools which was implemented on MATLAB 7.6.0 (R2008a). The method produces crisp numerical outcomes that evaluate the student’s academic performance. The student performance after the training of the two inputs was at the average for semester1 and semester 2.Â

Adaptive nonlinear control using fuzzy logic and neural networks

The problem of adaptive nonlinear control, i.e. the control of nonlinear dynamic systems with unknown parameters, is considered. Current techniques usually assume that either the control system is linearizable or the type of nonlinearity is known. This results in poor control quality for many practical problems. Moreover, the control system design becomes too complex for a practicing engineer. The objective of this thesis is to provide a practical, systematic approach for solving the problem of identification and control of nonlinear systems with unknown parameters, when the explicit linear parametrization is either unknown or impossible. Fuzzy logic (FL) and neural networks (NNs) have proven to be the tools for universal approximation, and hence are considered. However, FL requires expert knowledge and there is a lack of systematic procedures to design NNs for control. A hybrid technique, called fuzzy logic adaptive network (FLAN), which combines the structure of an FL controller with the learning aspects of the NNs is developed. FLAN is designed such that it is capable of both structure learning and parameter learning. Gradient descent based technique is utilized for the parameter learning in FLAN, and it is tested through a variety of simulated experiments in identification and control of nonlinear systems. The results indicate the success of FLAN in terms of accuracy of estimation, speed of convergence, insensitivity against a range of initial learning rates, robustness against sudden changes in the input as well as noise in the training data. The performance of FLAN is also compared with the techniques based on FL and NNs, as well as several hybrid techniques

Fuzzy Neural Network Models For Multispectral Image Analysis

Fuzzy neural networks (FNNs) provide a new approach for classification of multispectral data and to extract and optimize classification rules. Neural networks deal with issues on a numeric level, whereas fuzzy logic deals with them on a semantic or linguistic level. FNNs synthesize fuzzy logic and neural networks. Recently, there has been growing interest in the research community not only to understand how FNNs arrive at particular decisions but how to decode information stored in the form of connection strengths in the network. In this paper, we propose fuzzy neural network models for classification of pixels in multispectral images and to extract fuzzy classification rules. During the training phase, the connection strengths are updated. After training, classification rules are extracted by backtracking along the weighted paths through the FNN. The extracted rules are then optimized using a fuzzy associative memory (FAM) bank. The data mining system described above is useful in many practical applications such as mapping, monitoring and managing our planet’s resources and health, climate change impacts and assessments, environmental change detection and military reconnaissance

CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

An intelligent navigator for mobile vehicles

This paper presents an intelligent navigation method for navigation of a mobile vehicle in unknown environments. The proposed navigator consists of three modules: Obstacle Avoidor, Environment Evaluator and Navigation Supervisor. The Obstacle Avoidor is a fuzzy controller whose rule base is learnt through reinforcement learning. A new and powerful training method is proposed to construct the fuzzy rules automatically. The Navigation Supervisor determines the tactical requirement of avoiding obstacles or moving towards the goal location at each action step so that the vehicle can achieve its task without colliding with obstacles. The effectiveness of the learning method and the whole navigator are verified by simulation.published_or_final_versio

MICROTUBULE BASED NEURO-FUZZY NESTED FRAMEWORK FOR SECURITY OF CYBER PHYSICAL SYSTEM

Network and system security of cyber physical system is of vital significance in the present information correspondence environment. Hackers and network intruders can make numerous fruitful endeavors to bring crashing of the networks and web services by unapproved interruption. Computing systems connected to the Internet are stood up to with a plenty of security threats, running from exemplary computer worms to impart drive by downloads and bot networks. In the most recent years these threats have achieved another nature of automation and sophistication, rendering most defenses inadequate. Ordinary security measures that depend on the manual investigation of security incidents and attack advancement intrinsically neglect to give an assurance from these threats. As an outcome, computer systems regularly stay unprotected over longer time frames. This study presents a network intrusion detection based on machine learning as a perfect match for this issue, as learning strategies give the capacity to naturally dissect data and backing early detection of threats. The results from the study have created practical results so far and there is eminent wariness in the community about learning based defenses. Machine learning based Intrusion Detection and Network Security Systems are one of these solutions. It dissects and predicts the practices of clients, and after that these practices will be viewed as an attack or a typical conduct