# An Improved Brain-Inspired Emotional Learning Algorithm for Fast Classification
## 1 Introduction  
Classification has been widely used in the area of machine learning, pattern recognition and data mining. Various artificial intelligence methods, such as the artificial neural network (ANN) [1], support vector machine (SVM) [2], decision tree [3], extreme learning machine [4], the linear regression classifier [5] and other classifiers, have been proposed for classification problems. Among these methods, ANN is very popular due to the features of self-learning, self-adaptive and high generalization capability. However, the traditional ANN has been proven to have some significant drawbacks, such as low training speed, high computational complexity, and the convergence rate makes it hard to meet the requirements of real-time classification [6]. Recently, owing to the neurobiology and cognition research of emotion, emotional intelligence is playing an important role in artificial intelligence [7], and it has attracted an increasing interest around the world. Many bio-inspired brain emotional learning (BEL) models have been proposed and successfully applied in intelligent engineering applications [8; 9]. These models are based on the amygdala-orbitofrontal model proposed by [PERSON] et al. [10], which was inspired by the [PERSON]'s anatomical findings of the limbic system in mammalian brain [11]. According to the [PERSON]' finding, amygdala and orbitofrontal cortex are two main parts of the limbic system, and they interact to process emotional stimulus correctly and rapidly. Therefore, BEL-based models that mimic the highspeed of the emotional learning mechanism in the limbic system have the superior features of fast learning and quickly reacting, and they are widely used in classification [12], prediction [13] and control applications [14]. In the amygdala-orbitofrontal model, the reward signal is important to adjust the weights of amygdala and orbitofrontal cortex in the emotional learning process, but it is not clearly defined so far. Many researchers have proposed different versions of BEL models based on the amygdala-orbitofrontal model, as well as different reward signal determinations. [PERSON] et al. [15] proposed the BEL-based controller and explicitly determined the reward signal as PID formulization, which has been successfully applied in intelligent engineering applications. [PERSON] et al. [16] applied a BEL model to prediction short-term traffic flow and defined the reward signal as the multiplication of some related weight. [PERSON] [17] presents a BEL-based architecture for chaotic time series prediction. Although these BEL-based models achieve success in applications, they are based on reinforcement learning to adjust the weights of amygdala and orbitofrontal cortex in the BEL model, and they cannot be generalized to other issues for their characteristic of model sensitive. [PERSON] et al. [18] proposed a novel BEL-based pattern recognizer, which employed a pattern-target method instead of the reward-based reinforcement learning to update the weights of amygdala and orbitofrontal cortex. Although this is a model free method, it reduces the precision of the learning process, and the classification accuracy needs to be further improved. In this study, we aim to propose a more accurate BEL algorithm for fast classification. It has been demonstrated that the neural network classification accuracy can be substantially improved by optimizing the weights of the network [19]. There are many optimization methods, such as genetic algorithm (GA) [20], particle swarm optimization (PSO) [21], differential evolution (DE) [22], and so on. Compared to GA, PSO and DE are relatively simple and easily converge, but they are easily trapped into local minimum in the searching process and show a slight compromise in accuracy. While GA can evolve the population to a better area of the search space and it has good global searching ability [19], the BEL algorithm has the advantage of fast learning, but shows a compromise in accuracy. We aim to optimize the BEL algorithm and make it more accurate.