Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems

This paper presents a novel application of a hybrid learning approach to the optimisation of membership and non-membership functions of a newly developed interval type-2 intuitionistic fuzzy logic system (IT2 IFLS) of a Takagi-Sugeno-Kang (TSK) fuzzy inference system with neural network learning capability. The hybrid algorithms consisting of decou- pled extended Kalman filter (DEKF) and gradient descent (GD) are used to tune the parameters of the IT2 IFLS for the first time. The DEKF is used to tune the consequent parameters in the forward pass while the GD method is used to tune the antecedents parts during the backward pass of the hybrid learning. The hybrid algorithm is described and evaluated, prediction and identification results together with the runtime are compared with similar existing studies in the literature. Performance comparison is made between the proposed hybrid learning model of IT2 IFLS, a TSK-type-1 intuitionistic fuzzy logic system (IFLS-TSK) and a TSK-type interval type-2 fuzzy logic system (IT2 FLS-TSK) on two instances of the datasets under investigation. The empirical comparison is made on the designed systems using three artificially generated datasets and three real world datasets. Analysis of results reveal that IT2 IFLS outperforms its type-1 variants, IT2 FLS and most of the existing models in the literature. Moreover, the minimal run time of the proposed hybrid learning model for IT2 IFLS also puts this model forward as a good candidate for application in real time systems

Interval type-2 intuitionistic fuzzy logic system for time series and identification problems - a comparative study

This paper proposes a sliding mode control-based learning of interval type-2 intuitionistic fuzzy logic system for time series and identification problems. Until now, derivative-based algorithms such as gradient descent back propagation, extended Kalman filter, decoupled extended Kalman filter and hybrid method of decoupled extended Kalman filter and gradient descent methods have been utilized for the optimization of the parameters of interval type-2 intuitionistic fuzzy logic systems. The proposed model is based on a Takagi-Sugeno-Kang inference system. The evaluations of the model are conducted using both real world and artificially generated datasets. Analysis of results reveals that the proposed interval type-2 intuitionistic fuzzy logic system trained with sliding mode control learning algorithm (derivative-free) do outperforms some existing models in terms of the test root mean squared error while competing favourable with other models in the literature. Moreover, the proposed model may stand as a good choice for real time applications where running time is paramount compared to the derivative-based models

Extended Kalman filter-based learning of interval type-2 intuitionistic fuzzy logic system

Fuzzy logic systems have been extensively applied for solving many real world application problems because they are found to be universal approximators and many methods, particularly, gradient descent (GD) methods have been widely adopted for the optimization of fuzzy membership functions. Despite its popularity, GD still suffers some drawbacks in terms of its slow learning and convergence. In this study, the use of decoupled extended Kalman filter (DEKF) to optimize the parameters of an interval type-2 intuitionistic fuzzy logic system of Tagagi-Sugeno-Kang (IT2IFLS-TSK) fuzzy inference is proposed and results compared with IT2IFLS gradient descent learning. The resulting systems are evaluated on a real world dataset from Australia’s electricity market. The IT2IFLS-DEKF is also compared with its type-1 variant and interval type-2 fuzzy logic system (IT2FLS). Analysis of results reveal performance superiority of IT2IFLS trained with DEKF (IT2IFLS-DEKF) over IT2IFLS trained with gradient descent (IT2IFLS-GD). The proposed IT2IFLS-DEKF also outperforms its type-1 variant and IT2FLS on the same learning platform

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions

Development of software sensors for on-line monitoring of bakers yeast fermentation process

Software sensors and bioprocess are well-established research areas which have much to offer each other. Under the perspective of the software sensors area, bioprocess can be considered as a broad application area with a growing number of complex and challenging tasks to be dealt with, whose solutions can contribute to achieving high productivity and high-quality products. Although throughout the past years in the field of software sensors and bioprocess, progress has been quick and with a high degree of success, there is still a lack of inexpensive and reliable sensors for on-line state and parameter estimation. Therefore, the primary objective of this research was to design an inexpensive measurement system for on-line monitoring of ethanol production during the backers yeast cultivation process. The measurement system is based on commercially available metal oxide semiconductor gas sensors. From the bioreactor headspace, samples are pumped past the gas sensors array for 10 s every five minutes and the voltage changes of the sensors are measured. The signals from the gas sensor array showed a high correlation with ethanol concentration during cultivation process. In order to predict ethanol concentrations from the data of the gas sensor array, a principal component regression (PCR) model was developed. For the calibration procedure no off-line sampling was used. Instead, a theoretical model of the process is applied to simulate the ethanol production at any given time. The simulated ethanol concentrations were used as reference data for calibrating the response of the gas sensor array. The obtained results indicate that the model-based calibrated gas sensor array is able to predict ethanol concentrations during the cultivation process with a high accuracy (root mean square error of calibration as well as the percentage error for the validation sets were below 0.2 gL-1 and 7 %, respectively). However the predicted values are only available every five minutes. Therefore, the following plan of the research goal was to implement an estimation method for continues prediction of ethanol as well as glucose, biomass and the growth rates. For this reason, two nonlinear extensions of the Kalman filter namely the extended Kalman filter (EKF) and the unscented Kalman filter (UKF) were implemented separately for state and parameter estimation. Both prediction methods were validated on three different cultivation with variability of the substrate concentrations. The obtained results showed that both estimation algorithms show satisfactory results with respect to estimation of concentrations of substrates 6 and biomass as well as the growth rate parameters during the cultivation. However, despite the easier implementation producer of the UKF, this method shows more accurate prediction results compared to the EKF prediction method. Another focus of this study was to design and implement an on-line monitoring and control system for the volume evaluation of dough pieces during the proofing process of bread making. For this reason, a software sensor based on image processing was designed and implemented for measuring the dough volume. The control system consists of a fuzzy logic controller which takes into account the estimated volume. The controller is designed to maintain the volume of the dough pieces similar to the volume expansion of a dough piece in standard conditions during the proofing process by manipulating the temperature of the proofing chamber. Dough pieces with different amounts of backers yeast added in the ingredients and in different temperature starting states were prepared and proofed with the supervision of the software sensor and the fuzzy controller. The controller was evaluated by means of performance criteria and the final volume of the dough samples. The obtained results indicate that the performance of the system is very satisfactory with respect to volume control and set point deviation of the dough pieces.Softwaresensoren und Bioprozese sind gut etablierte Forschungsgebiete, die sich gegenseitig viel befruchten können. Unter dem Blickwinkel der Softwaresensorik kann der Bioprozess als ein breites Anwendungsgebiet mit einer wachsenden Zahl komplexer und anspruchsvoller Aufgabenstellungen betrachtet werden, deren Lösung zur Erzielung hoher Produktivität und qualitativ hochwertiger Produkte beitragen kann. Obwohl in den letzten Jahren auf dem Gebiet der Softwaresensoren und des Bioprozesses rasch und mit großem Erfolg Untersuchung erzielt wurden, fehlt es immer noch an kostengünstigen und zuverlässigen Sensoren für die Online-Zustands- und Parameterschätzung. Daher war das primäre Ziel dieser Forschung die Entwicklung eines kostengünstigen Messsystems für die Online-Überwachung der Ethanolproduktion während des Kultivierungsprozesses von Backhefe. Das Messsystem basiert auf kommerziell erhältlichen Metalloxid-Halbleiter-Gassensoren. Die Headspace-Proben des Bioreaktors werden alle fünf Minuten für 10 s an der Gassensor-Anordnung vorbeigepumpt und die Spannungsänderungen der Sensoren werden gemessen. Die Signale des Gassensorarrays zeigten eine hohe Korrelation mit der Ethanolkonzentration während des Kultivierungsprozesses. Um die Ethanolkonzentrationen aus den Daten des Gassensorarrays vorherzusagen, wurde ein Hauptkomponenten-Regressionsmodell (PCR) verwendet. Für das Kalibrierungsverfahren ist keine Offline-Probenahme notwendig. Stattdessen wird ein theoretisches Modell des Prozesses genutzt, um die Ethanolproduktion zu jedem beliebigen Zeitpunkt zu simulieren. Die kinetischen Parameter des Modells werden im Rahmen der Kalibration bestimmt. Die simulierten Ethanolkonzentrationen wurden als Referenzdaten für die Kalibrierung des Ansprechverhaltens des Gassensorarrays verwendet. Die erhaltenen Ergebnisse zeigen, dass das modellbasierte kalibrierte Gassensorarray in der Lage ist, die Ethanolkonzentrationen während des Kultivierungsprozesses mit hoher Genauigkeit vorherzusagen (der mittlere quadratische Fehler der Kalibrierung sowie der prozentuale Fehler für die Validierungssätze lagen unter 0,2 gL-1 bzw. 7 %). Die vorhergesagten Werte sind jedoch nur alle fünf Minuten verfügbar. Daher war der folgende Plan der Untersuchung die Implementierung einer Schätzmethode zur kontinuierlichen Vorhersage von Ethanol sowie von Glukose, Biomasse und der Wachstumsrate. Aus diesem Grund wurden zwei nichtlineare Erweiterungen des Kalman Filters, nämlich der erweiterte Kalman Filter (EKF) und der unscented Kalman Filter (UKF), getrennt für die Zustands und Parameterschätzung implementiert. Beide 8 Vorhersagemethoden wurden an drei verschiedenen Kultivierungen mit Variabilität der Start substratkonzentrationen validiert. Die erhaltenen Ergebnisse zeigen, dass beide Schätzungsalgorithmen zufriedenstellende Ergebnisse hinsichtlich der Schätzung der Konzentrationen von Substraten und Biomasse sowie der Parameter der Wachstumsrate während der Kultivierung ermitteln. Trotz der einfacheren Implementierung des UKF zeigt diese Methode jedoch genauere Vorhersageergebnisse im Vergleich zur EKF-Vorhersagemethode. Ein weiterer Schwerpunkt dieser Untersuchung war der Entwurf und die Implementierung eines Online-Überwachungs- und Regelungssystems für die Volumenauswertung von Teigstücken während des Gärprozesses bei der Brotherstellung. Aus diesem Grund wurde ein auf Bildverarbeitung basierendes Überwachungssystem zur Messung der Teigvolumenauswertung entworfen und implementiert. Das Regelsystem besteht aus einem Fuzzy-Logic-Controller, der das gemessene Volumen für die Regelung nutzt. Die Regelung ist so ausgelegt, dass das Volumen der Teiglinge mit Werten des Volumens eines Teiglings unter Standardbedingungen während des Gärprozesses vergleicht und die Temperatur der Gärkammer entsprechend anpasst. Teiglinge mit unterschiedlichen Hefemengen in den Zutaten und verschiedenen Temperaturstartwerten wurden vorbereitet und unter Anwendung des Fuzzy-Reglers gegärt. Der Regler wurde anhand von Leistungskriterien und dem Endvolumen der Teigproben bewertet. Die erhaltenen Ergebnisse zeigen, dass die Leistung des Systems in Bezug auf die Volumenregelung und die Sollwertabweichung der Teigstücke sehr zufriedenstellend ist