An immune algorithm based fuzzy predictive modeling mechanism using variable length coding and multi-objective optimization allied to engineering materials processing

In this paper, a systematic multi-objective fuzzy modeling approach is proposed, which can be regarded as a three-stage modeling procedure. In the first stage, an evolutionary based clustering algorithm is developed to extract an initial fuzzy rule base from the data. Based on this model, a back-propagation algorithm with momentum terms is used to refine the initial fuzzy model. The refined model is then used to seed the initial population of an immune inspired multi-objective optimization algorithm in the third stage to obtain a set of fuzzy models with improved transparency. To tackle the problem of simultaneously optimizing the structure and parameters, a variable length coding scheme is adopted to improve the efficiency of the search. The proposed modeling approach is applied to a real data set from the steel industry. Results show that the proposed approach is capable of eliciting not only accurate but also transparent fuzzy models

Multi-objective genetic optimisation for self-organising fuzzy logic control

This is the post-print version of the article. The official published version can be accessed from the link below.A multi-objective genetic algorithm is developed for the purpose of optimizing the rule-base of a Self-Organising Fuzzy Logic Control algorithm (SOFLC). The tuning of the SOFLC optimization is based on selection of the best shaped performance index for modifying the rule-base on-line. A comparative study is conducted between various methods of multi-objective genetic optimisation using the SOFLC algorithm on the muscle relaxant anaesthesia system, which includes a severe non-linearity, varying dynamics and time-delay

An artificial immune systems based predictive modelling approach for the multi-objective elicitation of Mamdani fuzzy rules: a special application to modelling alloys

In this paper, a systematic multi-objective Mamdani fuzzy modeling approach is proposed, which can be viewed as an extended version of the previously proposed Singleton fuzzy modeling paradigm. A set of new back-error propagation (BEP) updating formulas are derived so that they can replace the old set developed in the singleton version. With the substitution, the extension to the multi-objective Mamdani Fuzzy Rule-Based Systems (FRBS) is almost endemic. Due to the carefully chosen output membership functions, the inference and the defuzzification methods, a closed form integral can be deducted for the defuzzification method, which ensures the efficiency of the developed Mamdani FRBS. Some important factors, such as the variable length coding scheme and the rule alignment, are also discussed. Experimental results for a real data set from the steel industry suggest that the proposed approach is capable of eliciting not only accurate but also transparent FRBS with good generalization ability

Effect of changing strain rate on flow stress during hot deformation of type 316L stainless steel

ompression testing has been used with ramped changes in strain rate during deformation, and with changes in strain rate between double deformations to study their effects on flow stress. No systematic deviations from a mechanical equation of state were found for ramped increase or decrease in strain rate, even at the highest experimental ramping rates. In the two deformation tests, static recovery between deformations reduced the initial flow stress below the value for an equation of state by an amount dependent on time. The reduction was increased when strain rate was increased for the second deformation, and the strain interval required to re-establish the equation of state flow stress is uniquely related to the initial reduction in stress

Designing power system stabilizer for multimachine power system using neuro-fuzzy algorithm

This paper describes a design procedure for a fuzzy logic based power system stabilizer (FLPSS) and adaptive neuro-fuzzy inference system (ANFIS) and investigates their robustness for a multi-machine power system. Speed deviation of a machine and its derivative are chosen as the input signals to the FLPSS. A four-machine and a two-area power system is used as the case study. Computer simulations for the test system subjected to transient disturbances i.e. a three phase fault, were carried out and the results showed that the proposed controller is able to prove its effectiveness and improve the system damping when compared to a conventional lead-lag based power system stabilizer controller

Evolutionary computing for metals properties modelling

This is a post print version of the article, the official published version can be obtained from the link below.During the last decade Genetic Programming (GP) has emerged as an efficient methodology for teaching computers how to program themselves. This paper presents research work which utilizes GP for developing mathematical equations for the response surfaces that have been generated using hybrid modelling techniques for predicting the properties of materials under hot deformation. Collected data from the literature and experimental work on aluminium are utilized as the initial training data for the GP to develop the mathematical models under different deformation conditions and compositions.Financial support from the UK EPSRC (Engineering and Physical Sciences Research Council) under grant number GR/R70514/01 was used in this study

Absolute electrical impedance tomography (aEIT) guided ventilation therapy in critical care patients: simulations and future trends

Thoracic electrical impedance tomography (EIT) is a noninvasive, radiation-free monitoring technique whose aim is to reconstruct a cross-sectional image of the internal spatial distribution of conductivity from electrical measurements made by injecting small alternating currents via an electrode array placed on the surface of the thorax. The purpose of this paper is to discuss the fundamentals of EIT and demonstrate the principles of mechanical ventilation, lung recruitment, and EIT imaging on a comprehensive physiological model, which combines a model of respiratory mechanics, a model of the human lung absolute resistivity as a function of air content, and a 2-D finite-element mesh of the thorax to simulate EIT image reconstruction during mechanical ventilation. The overall model gives a good understanding of respiratory physiology and EIT monitoring techniques in mechanically ventilated patients. The model proposed here was able to reproduce consistent images of ventilation distribution in simulated acutely injured and collapsed lung conditions. A new advisory system architecture integrating a previously developed data-driven physiological model for continuous and noninvasive predictions of blood gas parameters with the regional lung function data/information generated from absolute EIT (aEIT) is proposed for monitoring and ventilator therapy management of critical care patients

Modelling of dynamic recrystallisation of 316L stainless steel using a systems approach

This is the post print version of the article. The official published version can be obtained from the link below.Dynamic recrystallisation (DRX) is an important aspect for industrial applications in hot metal working. Although DRX has been known for more than thirty years, its mechanisms have never been precisely investigated, in part because it was not readily possible to make local texture measurements. In the present work, the material behaviour during DRX is investigated and modelled based on the microstructure of 316L stainless steel. The developed model is based on a constitutive equation Modelling technique which incorporates the strain, strain rate and instantaneous temperature for predicting the flow stress of material being deformed under hot conditions.Financial support from the UK EPSRC (Engineering and Physical Sciences Research Council) for their financial support under grant number GR/R70514/01 was used for this study

A New Fuzzy Modeling Framework for Integrated Risk Prognosis and Therapy of Bladder Cancer Patients

This paper presents a new fuzzy modelling approach for analysing censored survival data and finding risk groups of patients diagnosed with bladder cancer. The proposed framework involves a new procedure for integrating the frameworks of interval type-2 fuzzy logic and Cox modelling intrinsically. The output of this synergistic framework is a risk score/prognostics index which is indicative of the patient's level of mortality risk. A threshold value is selected whereby patients with risk scores that are greater than this threshold are classed as high risk patients and vice versa. Unlike in the case of black-box type modelling approaches, the paper shows that interpretability and transparency are maintained using the proposed fuzzy modelling framework

Modelling the flow behaviour, recrystallisation and crystallographic texture in hot deformed Fe-30wt%Ni Austenite

Abstract: The present work describes a hybrid modelling approach developed for predicting the flow behaviour, recrystallisation characteristics and crystallographic texture evolution in a Fe-30wt%Ni austenitic model alloy subjected to hot plane strain compression. A series of compression tests were performed at temperatures between 850 and 1050ºC and strain rates between 0.1 and 10 s-1. The evolution of grain structure, crystallographic texture and dislocation substructure was characterised in detail for a deformation temperature of 950ºC and strain rates of 0.1 and 10 s-1, using electron backscatter diffraction and transmission electron microscopy. The hybrid modelling method utilises a combination of empirical, physically-based and neuro-fuzzy models. The flow stress is described as a function of the applied variables of strain rate and temperature using an empirical model. The recrystallisation behaviour is predicted from the measured microstructural state variables of internal dislocation density, subgrain size and misorientation between subgrains using a physically-based model. The texture evolution is modelled using artificial neural networks