An AutoML based trajectory optimization method for long-distance spacecraft pursuit-evasion game

Current successes in artificial intelligence domain have revitalized interest in spacecraft pursuit-evasion game,which is an interception problem with a non-cooperative maneuvering target. The paper presents an automated machine learning(AutoML) based method to generate optimal trajectories in long-distance scenarios. Compared with conventional deep neural network(DNN) methods, the proposed method dramatically reduces the reliance on manual intervention and machine learning expertise. Firstly, b...     

Novel algorithm for constructing support vector machine regression ensemble

1 .INTRODUCTIONRecently , support vector machine (SVM)[1]is anovel and promising technique in the fields of ma-chine learning and classification or regression pre-diction accompanying artificial neural network.InSVM,several learning algorithms can be obtainedgiven different inner-product functions named ker-nel functions ,such as polynomial approach,Bayes-ian classification、radial basic function method、multilayer perceptron network[2]. By now,it hasbeen successfully applied in many ar…     

Forecasting China’s Foreign Trade Volume with a Kernel-Based Hybrid Econometric-Ai Ensemble Learning Approach

Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence （AI） models to predict China＇s foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression （EC-VAR） model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear analysis perspective. Then an artificial neural network （ANN） based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert＇s judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression （SVR） for ensemble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China＇s foreign trade volume prediction problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.     

A review of mobile robot motion planning methods:from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots. As the complexity and randomness of robot application scenarios increase, the planning capability of the classical hierarchical motion planners is challenged. With the development of machine learning, the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature. The DRL-based motion planner is model-free and does not rely on the prior stru...     

Attributes-based person re-identification via CNNs with coupled clusters loss

Person re-identification(re-id) involves matching a person across nonoverlapping views, with different poses, illuminations and conditions. Visual attributes are understandable semantic information to help improve the issues including illumination changes, viewpoint variations and occlusions. This paper proposes an end-to-end framework of deep learning for attribute-based person re-id. In the feature representation stage of framework, the improved convolutional neural network(CNN) model is desig...     

NSNO: Neumann Series Neural Operator for Solving Helmholtz Equations in Inhomogeneous Medium

In this paper, the authors propose Neumann series neural operator(NSNO) to learn the solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to solutions.Helmholtz equation is a crucial partial differential equation(PDE) with applications in various scientific and engineering fields. However, efficient solver of Helmholtz equation is still a big challenge especially in the case of high wavenumber. Recently, deep learning has shown great potential in solving PDEs ...     

Deterministic Pattern Classifier Based on Genetic Programming

1　 IntroductionGenetic algorithm( GA) is a heuristic probability search method[1 ] ,and of which geneticprogramming ( GP) is an important branch.GP paradigm continues the trend of dealingwith the problem encoding in GA by increasing the complexity of the chromosomestructures undergoing adaptation.In particular,the chromosome structures for adaptationin GA are more general,hierarchical computer programs of dynamically varying size andshape.Many seemingly different problems in artificial in…     

Improve Robustness and Accuracy of Deep Neural Network with L2,∞ Normalization

In this paper, the L_2,∞ normalization of the weight matrices is used to enhance the robustness and accuracy of the deep neural network(DNN) with Relu as activation functions. It is shown that the L_2,∞ normalization leads to large dihedral angles between two adjacent faces of the DNN function graph and hence smoother DNN functions, which reduces over-fitting of the DNN. A global measure is proposed for the robustness of a classification DNN, which is the average radius of th...     

Number of Solitons Emerged in the Initial Profile of Shallow Water Using Convolutional Neural Networks

The soliton resolution conjecture proposes that the initial value problem can evolve into a dispersion part and a soliton part. However, the problem of determining the number of solitons that form in a given initial profile remains unsolved, except for a few specific cases. In this paper, the authors use the deep learning method to predict the number of solitons in a given initial value of the Korteweg-de Vries(Kd V) equation. By leveraging the analytical relationship between Asech2(x) initial v...     

A sparse algorithm for adaptive pruning least square support vector regression machine based on globalrepresentative point ranking

Least square support vector regression(LSSVR)is a method for function approximation,whose solutions are typically non-sparse,which limits its application especially in some occasions of fast prediction.In this paper,a sparse algorithm for adaptive pruning LSSVR algorithm based on global representative point ranking(GRPR-AP-LSSVR)is proposed.At first,the global representative point ranking(GRPR)algorithm is given,and relevant data analysis experiment is implemented which depicts the importance ranking of data points.Furthermore,the pruning strategy of removing two samples in the decremental learning procedure is designed to accelerate the training speed and ensure the sparsity.The removed data points are utilized to test the temporary learning model which ensures the regression accuracy.Finally,the proposed algorithm is verified on artificial datasets and UCI regression datasets,and experimental results indicate that,compared with several benchmark algorithms,the GRPR-AP-LSSVR algorithm has excellent sparsity and prediction speed without impairing the generalization performance.     

Improved artificial bee colony algorithm with mutual learning

The recently invented artificial bee colony (ABC) algorithm is an optimization algorithm based on swarm intelligence that has been used to solve many kinds of numerical function optimization problems.It performs well in most cases,however,there still exists an insufficiency in the ABC algorithm that ignores the fitness of related pairs of individuals in the mechanism of finding a neighboring food source.This paper presents an improved ABC algorithm with mutual learning (MutualABC) that adjusts the produced candidate food source with the higher fitness between two individuals selected by a mutual learning factor.The performance of the improved MutualABC algorithm is tested on a set of benchmark functions and compared with the basic ABC algorithm and some classical versions of improved ABC algorithms.The experimental results show that the MutualABC algorithm with appropriate parameters outperforms other ABC algorithms in most experiments.     

A UAV collaborative defense scheme driven by DDPG algorithm

The deep deterministic policy gradient(DDPG) algorithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration. Using the DDPG algorithm, agents can explore and summarize the environment to achieve autonomous decisions in the continuous state space and action space. In this paper, a cooperative defense with DDPG via swarms of unmanned aerial vehicle(UAV) is developed and validated, which has shown promising practical value...     

Generalized End-to-End Loss for Forensic Speaker Verification

In recent years, various speech embedding methods based on deep learning have been proposed and have shown better performance in speaker verification. Those new technologies will inevitably promote the development of forensic speaker verification. We propose a new forensic speaker verification method based on embeddings trained with loss function called generalized end-to-end(GE2E)loss. First, a long short-term memory(LSTM) based deep neural network(DNN) is trained as the embedding extractor, th...     

A Credit Risk Evaluation Approach to Neural Network Training by Means of Financial Ratios

In recent years artificial neural networks are used to recognize the risk category of investigated companies. The research is based on data from 81 listed enterprises that applied for credit in domestic regional banks operating in China. The backpropagation algorithm-the multilayer feedforward network structure is described. Each firm is described by 9 diagnostic variables and potential borrowers are classified into four classes. The efficiency of classification is evaluated in terms of classification errors calculated from the actual classification made by the credit officers. The results of the experiments show that LevenbergMarque training error is smallest among 4 learning algorithms and its performance is better, and application of artificial neural networks and classification functions can support the creditworthiness evaluation of borrowers.     

PD-type iterative learning control for nonlinear time-delay system with external disturbance

1 .INTRODUCTIONTheiterativelearning control method was proposedby Ariomoto et al[1]for the control system, whichcan performthe same task repetitively . The basiclearning controller for generating the present con-trol input is based on the previous control historyand a learning mechanism.In the last two decadesiterative learning control has been extensivelystudied and achieved significant progress in boththeory and application,and becomes the one of themost active fields inintelligent cont…     

Multi-agent system application in accordance with game theory in bi-directional coordination network model

The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents’ individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training...     

DIUN: Deeper Inception U-Network for Recovering Partial Pixelated Images

In our daily life, it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes. To prevent these phenomena and recover the true information from pixelated images,it is meaningful to research an effective reconstruction method for recovering pixelated images. This paper aims at recovering the artificial partial pixelated images via deep learning(DL). To abstract more ...     

“Towards Re-Inventing Psychohistory”:Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.     

ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR) imaging has been successfully addressed by popular motion compensation algorithms. However, when the target’s rotational velocity is sufficiently high during the dwell time of the radar, such compensation algorithms cannot obtain a high quality image. This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm. The keystone transform is used to coarsely compensate for the...     

Fuzzy Syntactic Congruence and Rational Fuzzy Languages

1　 IntroductionSince E.T.Lee and L.A.Zadeh gave the conceptoffuzzy finite-state automaton in1 969[2 ] ,the theory has been developed by researchers[1 ,3,4] . In this paper the fuzzy syntacticcongruence—another characterization of rational fuzzy language is dicussed.In the following text,we suppose thatΣ is an alphabetwith 1 |Σ|<∞ ,andΣ* isa free monoid generated fromΣ with the operation of adjoin,and F(Σ) denotes the setofall fuzzy subsets ofΣ,P(Σ) denotes the power setofΣ ,and …