scholarly journals LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning

2018 ◽  
Author(s):  
Marcel Steinmetz ◽  
Joerg Hoffmann
Keyword(s):  
State Space ◽  
State Equation ◽  
The State ◽  
Convergence Properties ◽  
Performance Improvements ◽  
State Space Search ◽  
Refinement Method ◽  
Significant Performance

Two strands of research in classical planning are LP heuristics and conjunctions to improve approximations. Combinations of the two have also been explored. Here, we focus on convergence properties, forcing the LP heuristic to equal the perfect heuristic h* in the limit. We show that, under reasonable assumptions, partial variable merges are strictly dominated by the compilation Pi^C of explicit conjunctions, and that both render the state equation heuristic equal to h* for a suitable set C of conjunctions. We show that consistent potential heuristics can be computed from a variant of Pi^C, and that such heuristics can represent h* for suitable C. As an application of these convergence properties, we consider sound nogood learning in state space search, via refining the set C. We design a suitable refinement method to this end. Experiments on IPC benchmarks show significant performance improvements in several domains.

scholarly journals Beyond Forks: Finding and Ranking Star Factorings for Decoupled Search

2017 ◽  
Author(s):  
Daniel Gnad ◽  
Valerie Poser ◽  
Jörg Hoffmann
Keyword(s):  
State Space ◽  
Reduction Method ◽  
Prior Work ◽  
Star Topology ◽  
Performance Improvements ◽  
State Space Search ◽  
Significant Performance ◽  
Overall Performance ◽  
Planning Task ◽  
Search Reduction

Star-topology decoupling is a recent search reduction method for forward state space search. The idea basically is to automatically identify a star factoring, then search only over the center component in the star, avoiding interleavings across leaf components. The framework can handle complex star topologies, yet prior work on decoupled search considered only factoring strategies identifying fork and inverted-fork topologies. Here, we introduce factoring strategies able to detect general star topologies, thereby extending the reach of decoupled search to new factorings and to new domains, sometimes resulting in significant performance improvements. Furthermore, we introduce a predictive portfolio method that reliably selects the most suitable factoring for a given planning task, leading to superior overall performance.

scholarly journals Parameter and State Estimator for State Space Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruifeng Ding ◽  
Linfan Zhuang
Keyword(s):  
State Space ◽  
State Equation ◽  
The State ◽  
Parameter Estimates ◽  
Identification Algorithm ◽  
State Variables ◽  
Input Output ◽  
State Estimator ◽  
Output Data ◽  
Canonical State

This paper proposes a parameter and state estimator for canonical state space systems from measured input-output data. The key is to solve the system state from the state equation and to substitute it into the output equation, eliminating the state variables, and the resulting equation contains only the system inputs and outputs, and to derive a least squares parameter identification algorithm. Furthermore, the system states are computed from the estimated parameters and the input-output data. Convergence analysis using the martingale convergence theorem indicates that the parameter estimates converge to their true values. Finally, an illustrative example is provided to show that the proposed algorithm is effective.

scholarly journals Efficient Temporal Planning Using Metastates

2019 ◽  
Vol 33 ◽  
pp. 7554-7561 ◽  
Author(s):  
Amanda Coles ◽  
Andrew Coles ◽  
J. Christopher Beck
Keyword(s):  
State Space ◽  
State Of The Art ◽  
The State ◽  
The Other ◽  
Temporal Constraints ◽  
State Space Explosion ◽  
Temporal Planning ◽  
State Space Search ◽  
Temporal Inconsistency

When performing temporal planning as forward state-space search, effective state memoisation is challenging. Whereas in classical planning, two states are equal if they have the same facts and variable values, in temporal planning this is not the case: as the plans that led to the two states are subject to temporal constraints, one might be extendable into at temporally valid plan, while the other might not. In this paper, we present an approach for reducing the state space explosion that arises due to having to keep many copies of the same ‘classically’ equal state – states that are classically equal are aggregated into metastates, and these are separated lazily only in the case of temporal inconsistency. Our evaluation shows that this approach, implemented in OPTIC and compared to existing state-of-the-art memoisation techniques, improves performance across a range of temporal domains.

Hydraulic-seasonal-time-based state space model for displacement monitoring of high concrete dams

2021 ◽  
pp. 014233122110183
Author(s):  
Shaowei Wang ◽  
Cong Xu ◽  
Chongshi Gu ◽  
Huaizhi Su ◽  
Bangbin Wu
Keyword(s):  
State Space ◽  
Elastic Deformation ◽  
State Space Model ◽  
State Equation ◽  
Time Effect ◽  
The State ◽  
Arch Dam ◽  
Model Parameters ◽  
Concrete Dams ◽  
Space Model

Displacement is the most intuitive reflection of the comprehensive behavior of concrete dams, especially the time effect displacement, which is a key index for the evaluation of the structural behavior and health status of a dam in long-term service. The main purpose of this paper is to establish a state space model for separating causal components from the measured dam displacement. This approach is conducted by initially proposing two equations, which are the state and observation equations, and model parameters are then optimized by the Kalman filter algorithm. The state equation is derived according to the creep deformation of dam concrete and foundation rock and is used to preliminarily predict the dam time effect displacement. Considering the generally recognized three components of dam displacement, the hydraulic-seasonal-time (HST) model is used to establish the observation equation, which is used to update the time effect displacement. The efficiency and rationality of the established state space model is verified by an engineering example. The results show that the hydraulic component separated by the state space model only contains the instantaneous elastic hydraulic deformation, while the hysteretic elastic hydraulic deformation is divided into the time effect component. The inverted elastic modulus of dam body concrete is an instantaneous value for the state space model but a comprehensive reflection of the instantaneous and hysteretic elastic deformation ability for the HST model, where the hysteretic elastic deformation is a part of the hydraulic component. For the Xiaowan arch dam, the inverted values are 42.9 and 36.7 GPa for the state space model and HST model, respectively. The proposed state space model is useful to improve the interpretation ability of the separated displacement components of concrete dams.

Application of local coordinates rectification in linearization of selected parameters of dynamic nonlinear systems

2014 ◽  
Vol 33 (5) ◽  
pp. 1819-1830 ◽  
Author(s):  
Andrzej Zawadzki
Keyword(s):  
Nonlinear System ◽  
Coordinate System ◽  
State Space ◽  
State Equation ◽  
The State ◽  
Content Type ◽  
Electric Circuits ◽  
First Order ◽  
State Space Transformation ◽  
Nonlinear State

Purpose – The purpose of this paper is to aim to an application of element of the theory of differential geometry for building the state space transformation, linearizing nonlinear dynamic system into a linear form. Design/methodology/approach – It is assumed that the description of nonlinear electric circuits with concentrated parameters or electromechanical systems is given by nonlinear system of differential equations of first order (state equations). The goal is to find transformation which leads nonlinear state equation (written in one coordinate system) to the linear in the other – sought coordinate system. Findings – The necessary conditions fulfilled by nonlinear system undergoing linearization process are presented. Numerical solutions of the nonlinear equations of state together with linearized system obtained from direct transformation of the state space are included (transformation input – the state of the nonlinear system). Originality/value – Application of first order exact differential forms for determining the transformation linearizing the nonlinear state equation. Simple linear models obtained with the use of the linearizing transformation are very useful (mainly because of the known and well-mastered theory of linear systems) in solving of various practical technical problems.

Study on Flange Bolted-Joints Structure Based on State Space Model

2011 ◽  
Vol 101-102 ◽  
pp. 1151-1155
Author(s):  
Mei Zi Tian ◽  
Deng Feng Zhao ◽  
Guo Ying Zeng ◽  
Hang Rui Yan
Keyword(s):  
State Space ◽  
Simulation Analysis ◽  
State Space Model ◽  
State Equation ◽  
The State ◽  
Bolted Joints ◽  
Vibration Test ◽  
Test Results ◽  
Bolted Connection ◽  
Space Model

Based on the vibration mechanics theory and system’s state equation, the state-space model of the flange bolted-joints structure is established. According to the dynamic characteristics of bolted connection, the parameters of the state-space model, such as rigidity and damping, can be identified. The accuracy of simulation model is validated, by comparing the simulation analysis results with the vibration test results, and an efficient method of recognition or fault diagnosis of bolted joints in vibratory environment is proposed.

scholarly journals Method Based on Rough Set and Improved Petri Net for Transformer Faults Diagnosis

2015 ◽  
Vol 11 (8) ◽  
pp. 25
Author(s):  
Chengyu Sun ◽  
XiaoGuang Yue
Keyword(s):  
State Space ◽  
Rough Set ◽  
Chromatographic Analysis ◽  
State Equation ◽  
The State ◽  
Transformer Oil ◽  
Ratio Method ◽  
Diagnostic Model ◽  
Matrix Operations ◽  
Calculation Results

Oil chromatographic analysis is widely used in transformer fault diagnosis, but it is difficult to establish accurate maping relationship between the parameter space and the state space, and there is information complexity. This paper adopts the combined diagnostic model of rough sets and petri nets, firstlysimplifies the complex system which contains complicated discrete information by rough set to solve the state space limitations of Petri network; and improves petri network based on mining association rules, adopts the correlation matrix and state equation method to improve the reasoning speed, at the same time turns the diagnosis into matrix operations to change complex calculations to simple math which has certain applicability. Finally the algorithm is applied to gas chromatographic analysis in transformer oil, the calculation results is the same with IEC three ratio method, which proves that this method can quickly and accurately to judge the running state of transformer, so as to improve the safety, stability and economic operation of the flat water transformer.

scholarly journals New Method of State-Space Formulation for Degenerate Circuit and Coupling Circuit

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Chung-Cheng Chen ◽  
Jian Ke ◽  
Yen-Ting Chen
Keyword(s):  
Differential Equation ◽  
State Space ◽  
State Equation ◽  
The State ◽  
New Method ◽  
Equation Approach ◽  
Electrical Field ◽  
Coupling Circuit ◽  
Space Formulation ◽  
Traditional Approaches

The state-space formulation overcomes many limitations of traditional differential equation approach and is utilized as alternative to many traditional approaches in the modern electrical field. This paper proposes a new method of finding the state equation for degenerate circuit and coupling circuit that have not been systematically solved now. This paper also introduces some sound improvements to solve complicated dependent-source circuits. Four comparative examples are demonstrated to show the significant merits that our method owns over the traditional approaches.

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

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