ABSTRACT
To understand the variants of Restricted Boltzmann Machines (RBMs) and Auto-encoders, we first need to outline what they are: RBMs are generative and unlike auto-encoders that are biased towards a limited set of data. RBMs are capable of generating new data with the joint set distribution. They are used to solve problems such as pattern recognition where there is a handwritten text that needs to be deciphered or a random pattern. It can also be used for recommendation engines where in collaboration with filtering techniques, recommendations are made to an end-user, and radar target recognition where it is used to detect intra-pulse with extremely low SNR and high noise. On the contrary, auto-encoders are not very commonly used in real-life applications; however, they are useful in reducing dimensionality and variational auto-encoders (VAE), where VAE learns the limitations of a probability distribution modeling the input data rather than learning the absolute function. RBMs and auto-encoders can be used for cyber-physical systems (CPS); RBMs are a dual-layer, two-part, erratic graphical model, allowing data to flow in two ways rather than one which forms the foundation of DBNs. CPS aims to use RBMs in making the model understand various functions, which will ultimately help to identify the hidden state and minimize the energy of the system. RBMs assign probabilities rather than definite values. Auto-encoders are unsupervised neural networks that use input vectors and try to match them to similar output vectors. These vectors are extremely skilled as they study compressed data encoding autonomously.
This chapter will discuss in detail the breakthroughs with CPS and their findings. Previously CPS were only evaluated with techniques that did not differentiate the facts from the internal view, which ultimately resulted in a mismatch between the behavior of theoretical models and their real-life counterparts. This ultimately gave way to the question of how they could perform critical safety tasks. Another vital breakthrough related to CPS revolves around Intellectual Merit and Broader Impacts. They both revolve around the design and analysis of Artificial Intelligence as an integral part of CPS; the findings for these breakthroughs will be discussed in detail in the chapter.
