A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.Comment: 30 pages, 20 figure

An Architecture for Performance Optimization in a Collaborative Knowledge-Based Approach for Wireless Sensor Networks

Over the past few years, Intelligent Spaces (ISs) have received the attention of many Wireless Sensor Network researchers. Recently, several studies have been devoted to identify their common capacities and to set up ISs over these networks. However, little attention has been paid to integrating Fuzzy Rule-Based Systems into collaborative Wireless Sensor Networks for the purpose of implementing ISs. This work presents a distributed architecture proposal for collaborative Fuzzy Rule-Based Systems embedded in Wireless Sensor Networks, which has been designed to optimize the implementation of ISs. This architecture includes the following: (a) an optimized design for the inference engine; (b) a visual interface; (c) a module to reduce the redundancy and complexity of the knowledge bases; (d) a module to evaluate the accuracy of the new knowledge base; (e) a module to adapt the format of the rules to the structure used by the inference engine; and (f) a communications protocol. As a real-world application of this architecture and the proposed methodologies, we show an application to the problem of modeling two plagues of the olive tree: prays (olive moth, Prays oleae Bern.) and repilo (caused by the fungus Spilocaea oleagina). The results show that the architecture presented in this paper significantly decreases the consumption of resources (memory, CPU and battery) without a substantial decrease in the accuracy of the inferred values

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

ObjectivesWe develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk.Methods and materialsThree FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model.ResultsIn an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008–2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results.ConclusionsThe main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings

Towards a many-valued logic of quantified belief

We consider a logic which "truth-values" are represented as quantified belief/disbelief pairs, thus integrating reports on how strongly the truth of a proposition is believed, and how strongly it is disbelieved. In this context a major motive for the logic proposed is, that it should not lead (as in classical logic) to irrelevant conclusions when contradictory beliefs are encountered. The logical machinery is built around the notion of the so-called logical klattice: a particular partial order on belief/disbelief pairs and fuzzy set-theoretic operators representing meet and join. A set of principles (semantically valid and complete) to be used in making inferences is proposed, and it is shown that they are a many-valued variant of the tautological entailments of relevance logic.To treat non truth-functional aspects of knowledge we introduce also the notion of the information lattice together with particular meet and join operators. These are used to provide answers to three fundamental questions: how to represent knowledge about belief/disbelief in the constituents of a formula when supplied with belief/disbelief about the formula as a whole; how to determine the amount of belief/disbelief to be assigned to formulas in an epistemic state (or a state of knowledge), that is, a collection of partial interpretations, and finally, how to change the present belief/disbelief in the truth of formulas, when provided with an input bringing in new belief/disbelief in the truth of these formulas. The answer to all these questions is given by defining a formula as a mapping from one epistemic state to a new state. Such a mapping is constructed as the minimum mutilation of the given epistemic state which makes a formula to be believed true (or false) in the new one. The entailment between formulas is also given the meaning of an input and its properties are studied.We study also if- then inference rules that are not pure tautological entailments, but rather express the causal relationship between the beliefs held with respect to the truth and falsity of the antecedent and the conclusion. Detachment operators are proposed to be used in cases when: (i) it is firmly believed that belief/disbelief in the validity of the conclusion follows from belief and/or disbelief in the validity of the antecedent, and (ii) it is believed, but only to a degree, that belief/disbelief in the validity of the conclusion follows from belief/disbelief in the validity of the antecedent. It is shown that the following four modes of inference are legitimated within the setting of these rules: modus ponens, modus tollens, denial, and confirmation.We consider also inference rules augmented with the so-called exeption condition: if IA/ then /BI unless IC/. The if- then part of the rule expresses the major relationship between A and B, i.e., it is believed (up to a degree) that belief and/or disbelief in Bfollows from belief and/or disbelief in A. Then the unless part acts as a switch that transforms the belief/disbelief pair of B from one expressing belief in its validity to oneindicating disbelief in the validity of B, whenever there is a meaningful enough belief in the exception condition C.We also give a meaning to the inference rules proposed as mappings from epistemic states to epistemic states, thus using them as a tool for changing already existing beliefs as well as for deriving new ones.</p