{'fraction_non_alphanumeric': 0.03531746031746032, 'fraction_numerical': 0.009523809523809525, 'mean_word_length': 5.016706443914081, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design

In recent years, the concept of the fuzzy logic or artificial neural networks for control problems has grown into a popular research area [1]-[3]. The reason is that classical control theory usually requires a mathematical model for designing controllers. The inaccuracy of mathematical modeling of plants usually degrades the performance of the controllers, especially for nonlinear and complex control problems [4], [25]. Fuzzy logic has the ability to express the ambiguity of human thinking and translate expert knowledge into computable numerical data. A fuzzy system consists of a set of fuzzy IF-THEN rules that describe the input-output mapping relationship of the networks. Obviously, it is difficult for human experts to examine all the input-output data from a complex system to find proper rules for a fuzzy system. To cope with this difficulty, several approaches that are used to generate the fuzzy IF-THEN rules from numerical data have been proposed [5]-[8]. These methods were developed for supervised learning; i.e., the correct “target” output values are given for each input pattern to guide the learning of the network. However, most of the supervised learning algorithms for neuro-fuzzy networks require precise training data to tune the networks for various applications. For some real world applications, precise training data are usually difficult and expensive, if not impossible, to obtain. For this reason, there has been a growing interest in reinforcement learning algorithms for use in fuzzy [9]-[10] or neural controller [11]-[12] design. In the design of a fuzzy controller, adjusting the required parameters is important. To do this, back-propagation (BP) training was widely used in [11]-[12], [18]. It is a powerful training technique that can be applied to networks with a forward structure. Since the steepest descent technique is used in BP training to minimize the error function, the algorithms may reach the local minima very fast and never find the global solution. The development of genetic algorithms (GAs) has provided another approach for adjusting parameters in the design of controllers. GA is a parallel and global technique [9], [19]. Because it simultaneously evaluates many points in a search space, it is more likely to converge toward the global solution. Some researchers have developed methods to design and implement fuzzy controllers by using GAs. Karr [2] used a GA to generate membership