Interval Type-2 Fuzzy Model for Intelligent Fire Intensity Detection Algorithm with Decision Making in Low-Power Devices

Local markets in East Africa have been destroyed by raging fires,leading to the loss of life and property in the nearby communities.Electrical circuits,arson,and neglected charcoal stoves are the major causes of these fires.Previous methods,i.e.,satellites,are expensive to maintain and cause unnecessary delays.Also,unit-smoke detectors are highly prone to false alerts.In this paper,an Interval Type-2 TSK fuzzy model for an intelligent lightweight fire intensity detection algorithm with decision-making in low-power devices is proposed using a sparse inference rules approach.A free open–source MATLAB/Simulink fuzzy toolbox integrated into MATLAB 2018a is used to investigate the performance of the Interval Type-2 fuzzy model.Two crisp input parameters,namely:FIT and FIG��are used.Results show that the Interval Type-2 model achieved an accuracy value of FIO�=98.2%,MAE=1.3010,MSE=1.6938 and RMSE=1.3015 using regression analysis.The study shall assist the firefighting personnel in fully understanding and mitigating the current level of fire danger.As a result,the proposed solution can be fully implemented in low-cost,low-power fire detection systems to monitor the state of fire with improved accuracy and reduced false alerts.Through informed decision-making in low-cost fire detection devices,early warning notifications can be provided to aid in the rapid evacuation of people,thereby improving fire safety surveillance,management,and protection for the market community.

Robust stabilitv of neutral stochastic interval svstems

Although interval systems have received a great deal of attention, so far there is few work on neutral stochastic interval systems (NSIS) . The purpose of this paper is to initiate the study of NSIS. Using R^umikhin-type technique, a sufficient condition of exponential stability on NSIS is given. It is interesting that the NSIS changes into general stochastic interval system when the neutral item disappeared. So the results in this paper generalize some conclusions existed.

Global stability of interval recurrent neural networks

The robust global exponential stability of a class of interval recurrent neural networks（RNNs） is studied,and a new robust stability criterion is obtained in the form of linear matrix inequality.The problem of robust stability of interval RNNs is transformed into a problem of solving a class of linear matrix inequalities.Thus,the robust stability of interval RNNs can be analyzed by directly using the linear matrix inequalities（LMI） toolbox of MATLAB.Numerical example is given to show the effectiveness of the obtained results.

LMI conditions for quadratic and robust D-stability of interval systems

Sufficient conditions for the quadratic D-stability and further robust D-stability of interval systems are presented in this paper. This robust D-stability condition is based on a parameter-dependent Lyapunov function obtained from the feasibility of a set of linear matrix inequalities （LMIs） defined at a series of partial-vertex-based interval matrices other than the total vertex matrices as in previous results. The results contain the usual quadratic and robust stability of continuous-time and discrete-time interval systems as particular cases. The illustrative example shows that this method is effective and less conservative for checking the quadratic and robust D-stability of interval systems.

Stabilization and H-infinity control for interval descriptor systems

This paper considers H-infinity control problem for interval descriptor systems. Necessary and sufficient LMI-based conditions are derived for quadratic-like H-infinity control analysis of interval descriptor systems. Using the analysis result, two types of feedback controllers are designed so that the closed-loop interval descriptor systems are admissible with H-infinity-norm less than a prescribed value. To the best of the authors＇s knowledge, this is the first paper to deal with the H-infinity control problem of interval descriptor systems in the literature.

Design of a robust PID-PSS for an uncertain power system with simplified stability conditions

In a deregulated power system uncertainty exists and lack of sufficient damping can lead to Low FrequencyOscillations (LFO). The problem can be addressed using robust Power System Stabilizers (PSS). In this paper, anoptimal procedure to design a robust PID-PSS using interval arithmetic for the Single Machine Infinite Bus (SMIB)power system is proposed. The interval modelling captures the wide variations of operating conditions in boundsof system coefficients. In the proposed design procedure, simple and new closed loop stability conditions for anSMIB interval system are developed and are used to design an optimum PID-PSS for improving the performance ofan SMIB system. The optimum PID-PSS is attained by tuning the parameters using the FMINCON tool provided inMATLAB. The robustness of the proposed PID-PSS design is validated and compared to other notable methods inthe literature when the system is subjected to different uncertainties. The simulation results and performance errorvalues show the effectiveness of the proposed robust PID-PSS controller.

Parallel Solutions for Large-Scale General Sparse Nonlinear Systems of Equations

In solving application problems, many largesscale nonlinear systems of equations result in sparse Jacobian matrices. Such nonlinear systems are called sparse nonlinear systems. The irregularity of the locations of nonzero elements of a general sparse matrix makes it very difficult to generally map sparse matrix computations to multiprocessors for parallel processing in a well balanced manner. To overcome this difficulty, we define a new storage scheme for general sparse matrices in this paper. With the new storage scheme, we develop parallel algorithms to solve large-scale general sparse systems of equations by interval Newton/Generalized bisection methods which reliably find all numerical solutions within a given domain.In Section 1, we provide an introduction to the addressed problem and the interval Newton's methods. In Section 2, some currently used storage schemes for sparse sys-terns are reviewed. In Section 3, new index schemes to store general sparse matrices are reported. In Section 4, we present a parallel algorithm to evaluate a general sparse Jarobian matrix. In Section 5, we present a parallel algorithm to solve the correspond-ing interval linear 8ystem by the all-row preconditioned scheme. Conclusions and future work are discussed in Section 6.

ROBUST ABSOLUTE STABILITY OF GENERAL LURIE SYSTEMS WITH MULTIPLE NONLINEARITIES

The robust absolute stability of general Lurie interval direct control system with multiple nonlinearities, with respect to model variations, is considered. Some sufficient conditions of absolute stability for the system are obtained, which generalize and improve the previous results.