{'fraction_non_alphanumeric': 0.02649228705566734, 'fraction_numerical': 0.00670690811535882, 'mean_word_length': 5.050709939148073, '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}}
Control of Nonlinear Systems via Temporal Difference Learning Based Intelligent Controller and Context Reasoning

Modeling emotions has attracted much attention in recent years, both in cognitive psychology and design of artificial systems. Far from being a negative factor in decisionmaking, emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference Learning. We applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling and system parameter robustness for TDBELBIC. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way. MODELLING Motivated by the success in functional modeling of emotions in control engineering applications [15,29,30], the main purpose of this research is to use a structural model based on the limbic system of mammalian brain, for decision making and control engineering applications. We have adopted a network model developed by Moren and Balkenius [21], as a computational model that mimics amygdala, orbitofrontal cortex, thalamus, sensory input cortex and generally, those parts of the brain thought responsible for processing emotions. There are two approaches to intelligent and cognitive control. In the indirect approach, the intelligent system is utilized for tuning the parameters of the controller. We have adopted the second, so called direct approach, where the intelligent system, in our case the computational model termed TDBELBIC, is used as the controller block. The model is illustrated in figure 1. TDBELBIC is essentially an action generation mechanism based on sensory inputs and emotional cues. In general, these can be vector valued, although in the benchmarks discussed in this paper for the sake of illustration, one sensory input and one emotional signal (stress) have been considered. The emotional learning occurs mainly in amygdala. The learning rule of amygdala is given in formula (1). ( ) A EC k Ga − = ∆ , 0 max . 1 (1) where a G is the gain in amygdala connection, 1 k is the learning step in amygdala and EC and A are the values of emotional cue function and amygdala output at each time. The term max in the formula (1) is for making the learning changes monotonic, implying that the amygdala gain can never be decreased. This rule is for modeling the incapability of unlearning the emotion signal (and consequently, emotional action), previously learned in the amygdala [21,23]. Similarly, the learning rule in orbitofrontal cortex is shown in formula (2). Orbitofrontal Cortex