A sigmoid function is necessary for creation a chaotic neural network (CNN). In this paper, a new function for CNN is proposed that it can increase the speed of convergence. In the proposed method, we use a novel signal for controlling chaos. Both the theory analysis and computer simulation results show that the performance of CNN can be improved remarkably by using our method. By means of this...

This study proposed a novel robust sliding mode control strategy for the stabilized platform of rotary steering drilling system. Firstly, a Nonlinear Disturbance Observer (NDO) which can converge exponentially with suitable design parameters is used to observe the uncertain disturbance of stabilized platform under work condition. Subsequently, sliding mode controller is designed to guarantee th...

We study model-based reinforcement learning (RL) for episodic Markov decision processes (MDP) whose transition probability is parametrized by an unknown core with features of state and action. Despite much recent progress in analyzing algorithms the linear MDP setting, understanding more general models very restrictive. In this paper, we propose a provably efficient RL algorithm given multinomi...

Background Brain amyloid-β (Aβ) is the pathological hallmark of Alzheimer’s disease (AD). In logistic models, Aβ accumulation a sigmoid function time-since-disease-onset (TSDO) (figure 1). Previous positron emission tomography (PET)-based models vary onset(t50) and duration(r) globally; capacity(K) baseline(NS) regionally (Whittington2018). We confirm existing approaches propose more powerful I...

Image enhancement is one of the most important issues in lowlevel image processing. Mainly, enhancement methods can be classified into two classes: global and local methods. Various enhancement schemes are used for enhancing an image which includes gray scale manipulation, filtering and Histogram Equalization (HE).Histogram Equalization (HE) has proved to be a simple and effective image contras...

Greenhouse operation and inside climate strongly depend on the outside weather. This implies that at least a year of data collection is required to cover the whole operational domain. Greenhouse-climate models calibrated with data limited to only a small region of the operating domain (weather and control), may therefore, produce erroneous predictions when applied to unfamiliar conditions. A co...

a sigmoid function is necessary for creation a chaotic neural network (cnn). in this paper, a new function for cnn is proposed that it can increase the speed of convergence. in the proposed method, we use a novel signal for controlling chaos. both the theory analysis and computer simulation results show that the performance of cnn can be improved remarkably by using our method. by means of this...