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Discusses automated trading systems modeling and trading algorithm structure from the perspective of system analysis and automatic control theory principles. We consider various modifications of trading algorithm implementation based on order book imbalance. Using computer simulation of the trading process we demonstrate various ideas and approaches to on- and off-line trading algorithm optimization,...
The problem of assessing the level of competence of students as a result of studying individual courses of the program in the process of instruction is considered, taking into account the vagueness of the formulation of competences as learning objectives and the evaluation of the learning outcomes of disciplines.
Robot Reinforcement Learning (RL) algorithms return a policy that maximizes a global cumulative reward signal but typically do not create diverse behaviors. Hence, the policy will typically only capture a single solution of a task. However, many motor tasks have a large variety of solutions and the knowledge about these solutions can have several advantages. For example, in an adversarial setting...
Marine robots and unmanned surface vehicles will increasingly be deployed in rivers and riverine environments. The structure produced by flowing waters may be exploited for purposes of estimation, planning, and control. This paper adopts a widely acknowledged model for the geometry of watercourse channels, namely sine-generated curves, as a basis for estimators that predict the shape of the yet unseen...
Humanlike robot skills, e.g., cleaning a table or handing over a plate, can often be generalized to different task variations. Usually, these are start-/goal position, and trained environment changes. We investigate how to modify motion primitives to context changes, which are not included in the training data. Specifically, we focus on maintaining humanlike motion characteristics and generalizability,...
The mechanism of the choice of the best forecast model on a set of experimental data on key performance indicators of corporate knowledges of the digital enterprise will be provided. Corporate knowledges of the digital enterprise is considered as the interconnected set of key performance indicators of management of various resources of the digital enterprise. Mechanisms of the choice of the best forecast...
We propose a sequential algorithm for learning sparse radial basis approximations for streaming data. The initial phase of the algorithm formulates the RBF training as a convex optimization problem with an objective function on the expansion weights while the data fitting problem imposed only as an ℓ∞-norm constraint. Each new data point observed is tested for feasibility, i.e., whether the data fitting...
Forces deployment optimization is a key problem of air defense command and control system. In this paper, we consider the hybrid deployment of multi-types of air defense weapons in multiple lines and sections. Firstly, the combat effectiveness of different types of air defense weapons is standardized. Secondly, the calculation model of probability of air attack targets penetration based on queuing...
We propose a new method to accelerate the convergence of optimization algorithms. This method simply adds a power coefficient γ ∊ [0, 1) to the gradient during optimization. We call this the Powerball method and analyze the convergence rate for the Powerball method for strongly convex functions and show that it has a faster convergence rate than gradient descent and Newton's method in the initial...
Differential dynamic programming (DDP) is a widely used trajectory optimization technique that addresses nonlinear optimal control problems, and can readily handle nonlinear cost functions. However, it does not handle either state or control constraints. This paper presents a novel formulation of DDP that is able to accommodate arbitrary nonlinear inequality constraints on both state and control....
Obtaining an unbiased data sample is an important task in the statistical analysis of experimental data. The unbiased data sample is a representative data sample. The natural desire is to obtain a representative data sample using computational methods. A procedure for adjusting the structure of the data sample in line with the structure of statistical population is called “correction of a data sample”...
An interval predictor model (IPM) is a computational model that predicts the range of an output variable given input-output data. This paper proposes strategies for constructing IPMs based on semidefinite programming and sum of squares (SOS). The models are optimal in the sense that they yield an interval valued function of minimal spread containing all the observations. Two different scenarios are...
We consider two close ways of linearization for sublinear operator that takes compact convex values. The first way consists in a representation of given multioperator by the family of so called basis selectors that are single-valued linear bounded operators. The second way consists in linear extension of given multioperator from its values on some Hamel basis. Every of the ways above leads to its...
The SLAM problem is known to have a special property that when robot orientation is known, estimating the history of robot poses and feature locations can be posed as a standard linear least squares problem. In this work, we develop a SLAM framework that uses relative feature-to-feature measurements to exploit this structural property of SLAM. Relative feature measurements are used to pose a linear...
Differential Evolution (DE) is a simple yet efficient stochastic algorithm for solving real world problems. Binomial crossover and exponential crossover are two commonly used crossover operators in current popular DE. It is noteworthy that these two operators can only generate a vertex of a hypercube defined by the mutant and target vectors. In this paper, using intermediate recombination to generate...
Attributes are human-annotated semantic descriptions of label classes. In zero-shot learning (ZSL), they are often used to construct a semantic embedding for knowledge transfer from known classes to new classes. While collecting all attributes for the new classes is criticized as expensive, a subset of these attributes are often easy to acquire. In this paper, we extend ZSL methods to handle this...
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the influence of its exponent remains poorly understood. In particular, it is unclear how the exponent of the reproducing kernel Hilbert space (RKHS) regularization term affects the accuracy and the efficiency of kernel-based...
In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis...
The contribution of the present work relies on an innovative and judicious combination of several optimization techniques for achieving high performance when using automatic vectorization and hybrid MPI/OpenMP parallelism in a Particle-in-Cell (PIC) code. The domain of application is plasma physics: the code simulates 2d2v Vlasov-Poisson systems on Cartesian grids with periodic boundary conditions...
Providing new parallel programming models/abstractions as a set of library functions has the huge advantage that it allows for an relatively easy incremental porting path for legacy HPC applications, in contrast to the huge effort needed when novel concepts are only provided in new programming languages or language extensions. However, performance issues are to be expected with fine granular usage...
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