Greenhouse Humidity Prediction and Control Models by Using Fuzzy Inference Systems

Wireless Multimedia Sensor Networks (WMSNs) are resource constrained (compared to the requirement of video communication), where the sensor nodes have limited bandwidth, energy, processing power and memory. Resource mapping is required in such networks, which is based on user requirements to offer better communication services as well as to use optimal resources. This paper proposes Mamdani's fuzzy inference system (FIS) to map the user requirement to resource demands by considering video/image. The input fuzzy parameters used are: available node energy, available bandwidth, and user quality needs. Output fuzzy parameter is frames to be transmitted per second (fps). By using ‘fps’ and the resolution of an image, the sensor node computes bandwidth and buffer requirements. The various defuzzification methods: centroid, smallest of maximum, and mean of maximum are used to get the response from the fuzzy inference system, and later they are compared.

With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

China started Sponge City Programme (SCP) to manage urban floods and to improve the quality of water environment in 2015. So far, the first two batches of pilot cities of SCP have been finished construction and two more batches of demonstration cities are ongoing. How to transfer the knowledge which learnt by the pilot cities to other cities is a key problem to enhance both the efficiency and the performance of SCP. This paper introduces the first step to develop a fuzzy system to represent knowledge which is acquired from stakeholders and decision makers, then to transform into if-then rules. This fuzzy logic inference system aims to simulate scenarios of real-life decision making. How to rate the performance of SCP in one city is chosen as an example in this paper. A Mamdani fuzzy inference system is applied, the fuzzy variables and fuzzy rules are hypothetical based on surveys of Qian’an, one of the pilot cities of SCP. Data from Qian’an is used to test and results show that the performance of SCP in Qian’an is good (74.1). The Mamdani fuzzy system is proved to be a powerful tool to represent knowledge and in the future other aspects of knowledge from SCP will be applied.

For a fuel cell bus, it is significant to equip the regenerative break system to enhance the fuel economy. A two-input, one-output Mamdani's fuzzy inference system is introduced to the regenerative braking model. Taking the safety working conditions of the batteries, the driving dynamics and the occupant comfort into consideration, the method can not only recycle the bus braking energy adequately, but also be adjusted easily according to the practical requirement. Through vehicle tests, the method proves to be a valid solution.

The article discusses the measurement model of tourism destination images perceived by travellers based on five dimensions of the service quality, i.e. tangibles, reliability, responsiveness, empathy, and assurance. Measurement problems occur when a traveller assesses the quality of a tourism destination service subjectively with vague boundaries and perceives the image of a tourism destination to vary. To address these issues, we designed an inference model using Mamdani's fuzzy inference system. The results of this study are quantitative assessments of the image of tourism destinations by various travellers based on qualitative perceptions of the quality of service experienced by the traveller.
 

In weather forecasting process, it is important to analyze weather radar data for classifying precipitation and non-precipitation echoes due to increase forecasting accuracy. There are lots of ongoing researches about non-precipitation echoes for detecting and removing them. In this paper, we present an algorithm to detect and remove line-shaped non-precipitation echo. The line-shaped echo can be divided into sun strobe echo that occurred by solar energy at sunrise or sunset and radial interference echo that occurred by electromagnetic interference wave. In order to detect and remove these two echoes, Mamdani fuzzy inference system is applied to combine expert's knowledge and characteristics. We tested the algorithm by applying the actual line-shaped echoes. And the result shows that the fuzzy inference system can distinguish not only sun strobe echo but also radial interference echo from actual radar data.

Robot human interaction requires use of safe, compliant and light weight actuators. Conventional linear motors and pneumatic cylinders are normally used to actuate robots to assist and augment human motions. Lately it has been realized that these actuators are not suitable and safe for applications involving human actor. Their large weight, size and stiffer design raise concerns. Pneumatic muscle actuators (PMA) on the other hand are very light weight, compact and have inherent compliance which make them potential candidate for applications involving robot human interaction. Taking on the advantages, these actuators are now being experimented for a variety of medical and rehabilitation applications. However they are not very popular due to their highly nonlinear and time dependent behavior which poses control problems. In this paper, an attempt is being made to accurately predict the uncertain and ambiguous characteristics of PMA using Artificial Intelligence (AI). Conventional tools such as analytical and numerical methods can only model a nonlinear system which is time independent. Time varying nonlinear system characteristics can be best modeled using artificial intelligence-based regression models. In this research, Artificial Neural Network (ANN), Mamdani Fuzzy Inference System (FIS) and Takagi-Sugeno (TS)-based fuzzy system are developed after carefully analyzing the time series data obtained from a real system. To achieve higher accuracy from these models, their parameters are tuned. Parameters of ANN are tuned using back propagation algorithm whereas fuzzy parameters are tuned using three different methods, namely, gradient descent method (GD), genetic algorithms (GA) and Modified Genetic Algorithm (MGA). It was found that the TS fuzzy inference system tuned by MGA provides better accuracy and can also model the time dependent behavior of PMA. The proposed TS fuzzy system is found to perform better in terms of accuracy and maximum deviation when compared to the previous approaches in the literature.

In many different fields, fuzzy inference systems (FIS) have become effective tools for managing complex and uncertain systems. The Takagi-Sugeno Fuzzy Model (TSFM) and the Mamdani Fuzzy Inference System (MFIS) are the two most used FIS models. The fuzzy inference processes used in these two models are compared in this research. The Mamdani Fuzzy Inference System uses membership functions and linguistic variables in conjunction with fuzzy rules to map input variables to output variables. To produce clear output values, it uses a fuzzy rule basis, fuzzy logic operators, and defuzzification approaches. The MFIS's capacity to capture linguistic information through rule-based modeling makes it especially appropriate for handling complicated and nonlinear systems. The Takagi-Sugeno Fuzzy Model, on the other hand, is a fuzzy rule-based model that uses a collection of linear or nonlinear functions to mimic the behavior of a system. The TSFM directly uses input variables to create rule consequents rather than using language variables. This model is renowned for its computing efficiency, interpretability, and simplicity. The operating principles, architecture, and salient features of the Takagi-Sugeno Fuzzy Model and the Mamdani Fuzzy Inference System are examined and contrasted in this research. The rule inference procedure, membership functions, aggregation procedures, and defuzzification strategies used in each model are all covered. It also outlines the advantages and disadvantages of both models in terms of handling uncertainty, computational efficiency, interpretability, and system modeling.

PurposeLimited facilities operating and modernization budgets require organizations to carefully identify, prioritize and authorize projects to ensure allocated resources align with strategic objectives. Traditional facility prioritization methods using risk matrices can be improved to increase granularity in categorization and avoid mathematical error or human cognitive biases. These limitations restrict the utility of prioritizations and if erroneously used to select projects for funding, they can lead to wasted resources. This paper aims to propose a novel facility prioritization methodology that corrects these assessment design and implementation issues.Design/methodology/approachA Mamdani fuzzy logic inference system is coupled with a traditional, categorical risk assessment framework to understand a facilities’ consequence of failure and its effect on an organization’s strategic objectives. Model performance is evaluated using the US Air Force’s facility portfolio, which has been previously assessed, treating facility replicability and interruptability as minimization objectives. The fuzzy logic inference system is built to account for these objectives, but as proof of ease-of-adaptation, facility dependency is added as an additional risk assessment criterion.FindingsResults of the fuzzy logic-based approach show a high degree of consistency with the traditional approach, though the value of the information provided by the framework developed here is considerably higher, as it creates a continuous set of facility prioritizations that are unbiased. The fuzzy logic framework is likely suitable for implementation by diverse, spatially distributed organizations in which decision-makers seek to balance risk assessment complexity with an output value.Originality/valueThis paper fills the identified need for portfolio management strategies that focus on prioritizing projects by risk to organizational operations or objectives.

Mamdani fuzzy inference system has been widely used for potential risk modelling and management. The decision-making is usually provided by multiple experts in the field. The conflicting information in sources from different experts become an open issue and has attracted some researchers to investigate further. Various risk factors in a project caused difficulties for decision makers to make reliable decisions on the whole project since it involves ambiguities, vagueness, and fuzziness. The introduction of the fuzzy inference system to the evaluation of construction risk is capable in explaining its reasoning process and, hence, overcoming such problems. Risk factors under the project management risk were identified through literature sources and from the opinion of experts. It is found that the likelihood and severity of risk is somehow interlinked with the concept of fuzzy theory. For model input and output linguistics variables, the triangular membership function was selected. The methodology employs a fuzzy aggregation system in which an appropriate control action can be determined by the acquisition of expert judgment. A total of 23 rules with logical OR operator, truncation implication, and Mean of Maxima (MoM) method for defuzzification were used to create an effective fuzzy model intended for making decisions. The framework determines the relationship between input and output parameters in if-then rules or mathematical functions using an effective fuzzy arithmetic operator. The study addresses the principle issues of multiexpert opinions based on Mamdani-type decision system and the illustrative example taken from one of medium-sized project held in Malaysia’s construction industry. By comparing with other experimental results, we verify the rationality and reliability of the proposed method.

Refer to this paper, an intelligent-fuzzy feed- forward computed torque estimator for Proportional- Integral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately and it is the most used in robot manipulators. In order to remove steady- state error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Computed torque compensation is one of the nonlinear compensator. The main problem of the pure computed torque compensator (CTC) was highly nonlinear dynamic parameters which related to system's dynamic parameters in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using feed-forward fuzzy inference system. To eliminate the continuum robot manipulator system's dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on design feed-forward fuzzy inference system and applied to CTC. The results demonstrate that the model base feed-forward fuzzy CTC estimator works well to compensate linear PID controller in presence of partly uncertainty system (e.g., continuum robot).

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Classifying study objects into groups is facilitated by fuzzy classifiers based on a set of rules and membership functions. Typically, the characteristics of the study objects are used to establish the criteria for classification. This work arises from the need to design fuzzy classifiers in contexts where real data is scarce or highly random, proposing a design based on statistics and chaotic maps that simplifies the design process. This study introduces the development of a fuzzy classifier, assuming that three features of the population to be classified are random variables. A Mamdani fuzzy inference system and three pseudorandom number generators based on one-dimensional chaotic maps are utilized to achieve this. The logistic, Bernoulli, and tent chaotic maps are implemented to emulate the random features of the target population, and their statistical distribution functions serve as input to the fuzzy inference system. Four experimental tests were conducted to demonstrate the functionality of the proposed classifier. The results show that it is possible to achieve a symmetric and robust classification through simple adjustments to membership functions, without the need for supervised training, which represents a significant methodological contribution, especially because this indicates that designers with minimal experience can build effective classifiers in just a few steps. Real applications of the proposed design may focus on the classification of biomedical signals (sEMG), network traffic, and personalized medical assistance systems, where data exhibits high variability and randomness.

Customer classification: A Mamdani fuzzy inference system standpoint for modifying the failure mode and effect analysis based three dimensional approach

Refer to this research, an intelligent robust fuzzy parallel feedback linearization estimator for ProportionalIntegral-Derivative (PID) controller is proposed for highly nonlinear continuum robot manipulator. In the absence of robot knowledge, PID may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately. And it is the most used in robot manipulators. In order to remove steadystate error caused by uncertainties and noise, the integrator gain has to be increased. This leads to worse transient performance, even destroys the stability. The integrator in a PID controller also reduces the bandwidth of the closed-loop system. Model-based compensation for PD control is an alternative method to substitute PID control. Feedback linearization compensation is one of the nonlinear compensator. The first problem o f the pure feedback linearization co mpensator (FLC) was equivalent problem in certain and uncertain systems. The nonlinear equivalent dynamic problem in uncertain system is solved by using parallel fuzzy logic theory. To eliminate the continuum robot man ipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to FLC. Th is methodology is based on design parallel fu zzy inference system and applied to equivalent nonlinear dynamic part of FLC. The results demonstrate that the model free fuzzy FLC estimator works well to co mpensate linear PID controller in presence of part ly uncertainty system (e.g., continuum robot).