Practical Application Of Fuzzy Logic Research Articles

Enhancing targeted tumor treatment: A novel fuzzy logic framework for precision drug delivery strategy selection

Objective:This study aims to address the challenge of selecting optimal drug delivery strategies for tumor patients by introducing a novel theoretical framework. Methods:We propose a fuzzy logic-based framework for quantitatively assessing Health States (HS) in tumor patients. This framework integrates quantified HS assessments with causality strength analyses, offering a comprehensive understanding of various drug delivery schemes’ effectiveness from pharmacokinetic and pharmacodynamic perspectives. Results:The efficacy of our approach is demonstrated through a series of real-world patient case studies, highlighting its potential to enhance the evaluation and selection of targeted drug delivery strategies. Conclusion:Our work contributes to the field by showcasing practical applications of fuzzy logic in targeted drug delivery systems (TDDs) and establishing a new benchmark for precision in drug delivery strategy selection. Significance:This study has significant implications for developing personalized medical treatments, potentially revolutionizing the field with a more nuanced and scientifically rigorous method for evaluating and selecting drug delivery protocols. Contributions:Development of a fuzzy logic framework for precise quantification of health states in tumor patients. Innovative integration of a causal system for comprehensively evaluating targeted drug delivery strategies.

Fuzzy logic as a decision-making support tool in planning transport development

Deliberations on transport development indicate that planning is its most significant aspect. One of the key issues in planning is selecting infrastructure projects for completion that will contribute to achieving the development objectives. The important functions of planning, as well as its complexity, indicate the need to use solutions in the decision-making support field. In Poland, in the area of strategic planning of infrastructure development, methods of supporting decision-making aimed at selecting infrastructure projects, taking into account their degree of compliance with strategic goals, are currently not applied comprehensively. The paper aims to address this gap with MCDA solution basing on review of literature combined with the authors’ experience in transport planning. Therefore, authors presented a proposed tool for supporting decision-making in planning transport development on a strategic level. The presented method allows for assessing infrastructure development projects in road and rail transport. Such assessments take into account a number of criteria corresponding to the main development directions, i.e. sustainable development and quality of life. Due to the method of formulating development objectives, it has been decided that it will be advantageous to apply fuzzy logic, which enables using natural language in decision-making support systems. To allow practical application of fuzzy logic, the Fuzzy Logic Toolbox package available in the MATLAB environment has been employed. The developed model contains a structure along with defined linguistic variables reflecting the decision-making criteria; also, it includes membership functions, inference rules as well as assessment results. The paper also defines the algorithm of decision-making support procedure. For verification purposes, the decision support model was applied in several real-life project evaluation cases, including a variety of projects in construction, development, and renovation of rail and road infrastructure. The deliberations described in this paper indicate the usefulness of fuzzy logic for supporting decision-making in planning transport development. It’s beneficial that the defined criteria can be applied in the case of projects in early preparation phase, enabling their practical application. Implementation of the solution in the MATLAB Fuzzy Logic Toolbox enables achieving fast results of the assessment of decision-maker preference level.

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making.

Multi-valued and Fuzzy Logic Realization using TaOx Memristive Devices

Among emerging non-volatile storage technologies, redox-based resistive switching Random Access Memory (ReRAM) is a prominent one. The realization of Boolean logic functionalities using ReRAM adds an extra edge to this technology. Recently, 7-state ReRAM devices were used to realize ternary arithmetic circuits, which opens up the computing space beyond traditional binary values. In this manuscript, we report realization of multi-valued and fuzzy logic operators with a representative application using ReRAM devices. Multi-valued logic (MVL), such as Łukasiewicz logic generalizes Boolean logic by allowing more than two truth values. MVL also permits operations on fuzzy sets, where, in contrast to standard crisp logic, an element is permitted to have a degree of membership to a given set. Fuzzy operations generally model human reasoning better than Boolean logic operations, which is predominant in current computing technologies. When the available information for the modelling of a system is imprecise and incomplete, fuzzy logic provides an excellent framework for the system design. Practical applications of fuzzy logic include, industrial control systems, robotics, and in general, design of expert systems through knowledge-based reasoning. Our experimental results show, for the first time, that it is possible to model fuzzy logic natively using multi-state memristive devices.

The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity

Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3’ UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic.A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry.When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing.

Is there a need for fuzzy logic?

Is there a need for fuzzy logic?

Conjunctors and their Residual Implicators: Characterizations and Construction Methods

In many practical applications of fuzzy logic it seems clear that one needs more flexibility in the choice of the conjunction: in particular, the associativity and the commutativity of a conjunction may be removed. Motivated by these considerations, we present several classes of conjunctors, i.e. binary operations on [0, 1] that are used to extend the boolean conjunction from {0, 1} to [0, 1], and characterize their respective residual implicators. We establish hence a one-to-one correspondence between construction methods for conjunctors and construction methods for residual implicators. Moreover, we introduce some construction methods directly in the class of residual implicators, and, by using a deresiduation procedure, we obtain new conjunctors.

Hybrid algorithm with fuzzy system and conventional PI control for the temperature control of TV glass furnace

This paper presents a practical application of fuzzy logic to the temperature control of glass melting furnace for television picture tube. Because of the complexity and nonlinearity, temperature control of glass melting furnace is still delegated to human operators. Though the overall characteristics of glass melting furnace are complex and nonlinear, one part of the furnace characteristics can be modeled as a linear system. The linear part of the furnace dynamics is modeled with a first-order-plus-dead-time (FOPDT) system and a proportional integral (PI) controller is applied to the FOPDT model. The remaining complex and nonlinear part of the furnace dynamics is covered by the fuzzy system, i.e., rules of human experts. The PI controller and fuzzy system are combined in cascade. The PI controller is applied to maintain the left-right symmetric operation of the furnace, and a linear regression is used for the temperature trend to determine input to the fuzzy system in order to overcome the high frequency disturbance. The proposed hybrid controller is implemented in Samsung-Corning Company to demonstrate the effectiveness of the proposed control algorithm.

Fuzzy sliding-mode controllers with applications

This paper concerns the design of robust control systems using sliding-mode control that incorporates a fuzzy tuning technique. The control law superposes equivalent control, switching control, and fuzzy control. An equivalent control law is first designed using pole placement. Switching control is then added to guarantee that the state reaches the sliding mode in the presence of parameter and disturbance uncertainties. Fuzzy tuning schemes are employed to improve control performance and to reduce chattering in the sliding mode. The practical application of fuzzy logic is proposed here as a computational-intelligence approach to engineering problems associated with sliding-mode controllers. The proposed method can have a number of industrial applications including the joint control of a hydraulically actuated mini-excavator as presented in this paper. The control hardware is described together with simulated and experimental results. High performance and attenuated chatter are achieved. The results obtained verify the validity of the proposed control approach to dynamic systems characterized by severe uncertainties.

Practical application of fuzzy logic and neural networks to fractured reservoir characterization

Until recently most fractured-reservoir modeling tools were limited to simple discrete statistical models. A new approach in fractured-reservoir characterization which uses artificial intelligence tools is described in this paper. The methodology is based on the assumption that there is a complex relationship between a large number of potential geologic drivers and fractures. Structure, bed thickness and lithology are a few of the drivers that played a role when fractures where created. The first step in the described methodology is the ranking of all existing geologic drivers. A fuzzy neural network is used to evaluate the hierarchical effect of each geologic driver on the fractures. As a result, the geologist or reservoir engineer will be able to identify locally and globally the key geologic drivers affecting fractures. The second step of the approach is to create a set of stochastic models using a backpropagation neural network that will try to quantify the underlying complex relationship that may exist between key geologic drivers and fracture intensity. The training and testing of the neural network is accomplished using existing data. The third step of the approach is to perform an uncertainty analysis by examining the fracture cumulative distribution function resulting from the large number of stochastic models. Using these three steps, the 2D or 3D distribution fractures and their underlying uncertainty can be determined at undrilled locations. The methodology is illustrated with an actual tight gas fractured sandstone.