In this chapter, we introduce some of the more popular ML algorithms. Our objective is to provide the basic concepts and main ideas, how to utilize these algorithms using Matlab, and offer some examples. In particular, we discuss essential concepts in feature engineering and how to apply them in Matlab. Support vector machines (SVM), K-nearest neighbor (KNN), linear regression, Naïve Bayes algorithm, and decision trees are introduced and the fundamental underlying mathematics is explained while using Matlab’s corresponding Apps to implement each of these algorithms. A special section on reinforcement learning is included, detailing the key concepts and basic mechanism of this third ML category. In particular, we showcase how to implement reinforcement learning in Matlab as well as make use of some of the Python libraries available online and show how to use reinforcement learning for controller design.

Starting with the perceptron, in Chapter 6 we discuss the functioning, the training, and the use of neural networks. For the different neural network structures, the corresponding script in Matlab is provided and the limitations of the different neural network architectures are discussed. A detailed discussion and the underlying mathematical concept of the Backpropagation learning algorithm is accompanied with simple examples as well as sophisticated implementations using Matlab. Chapter 6 also includesconsiderations on quality measures of trained neural networks, such as the accuracy, recall, specificity, precision, prevalence, and some of the derived quantities such as the F-score and the receiver operating characteristic plot. We also look at the overfitting problem and how to handle it during the neural network training process.

Silicon integrated circuits (ICs) are pervasive in our world, and the global semiconductor industry today exceeds $500 billion in annual sales. The devices and chips this industry produces support global industries, including consumer electronics, transportation, avionics and many others, that collectively represent a major part of global markets. Devices and chips built with other semiconductor materials such as GaAs, SiC and GaN provide critical components for specific application areas, including high-frequency communications systems, solid-state lighting and power management. It is not incorrect to say that the technical foundation of our modern world is based on semiconductors. The critical role that chips play has led to global competition to design, fabricate and build into advanced systems these remarkable components. Their importance to our world is unlikely to change in the foreseeable future.

Hybrid systems often try to leverage the advantages of one algorithm with the once of another while minimize its own disadvantages. Having discussed fuzzy logic and neural networks as well as a number of optimization algorithms, Chapter 7 presents several hybrid algorithms that can be used for optimization, controls, and modeling. In particular, we look at neural expert systems, expand these systems to neuro fuzzy systems and adaptive neuro-fuzzy inference systems, which we use for control applications. While revisiting the Mamdani and Sugeno fuzzy inference system, the Tsukamoto fuzzy system as well as different partitioning methods are discussed, such as the grid, the tree and the scatter partitioning. Examples using Matlab FIS app as well as Matlab’s ANFIS editor are used throughout the chapter.

Chapter 3 introduces the concept of the rule base along with the material from Chapter 2 to construct different fuzzy inference systems such as the Mamdani fuzzy inference system or the Sugeno fuzzy inference system. The Takagi-Sugeno fuzzy inference system is used to design fuzzy logic controllers and Lyapunov theory is utilized to investigate the closed-loop system stability of such controllers. Concepts such as local sector nonlinearity, globally asymptotical stability using state-space models are introduced and discussed to fashion controllers for nonlinear systems. Throughout the chapter, Matlab’s FIS editor is used to design fuzzy inference systems and corresponding controllers.