4,222 research outputs found
A Study of NK Landscapes' Basins and Local Optima Networks
We propose a network characterization of combinatorial fitness landscapes by
adapting the notion of inherent networks proposed for energy surfaces (Doye,
2002). We use the well-known family of landscapes as an example. In our
case the inherent network is the graph where the vertices are all the local
maxima and edges mean basin adjacency between two maxima. We exhaustively
extract such networks on representative small NK landscape instances, and show
that they are 'small-worlds'. However, the maxima graphs are not random, since
their clustering coefficients are much larger than those of corresponding
random graphs. Furthermore, the degree distributions are close to exponential
instead of Poissonian. We also describe the nature of the basins of attraction
and their relationship with the local maxima network.Comment: best paper nominatio
Physics-Informed Neural Networks for an optimal counterdiabatic quantum computation
We introduce a novel methodology that leverages the strength of
Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD)
protocol in the optimization of quantum circuits comprised of systems with
qubits. The primary objective is to utilize physics-inspired deep
learning techniques to accurately solve the time evolution of the different
physical observables within the quantum system. To accomplish this objective,
we embed the necessary physical information into an underlying neural network
to effectively tackle the problem. In particular, we impose the hermiticity
condition on all physical observables and make use of the principle of least
action, guaranteeing the acquisition of the most appropriate counterdiabatic
terms based on the underlying physics. The proposed approach offers a
dependable alternative to address the CD driving problem, free from the
constraints typically encountered in previous methodologies relying on
classical numerical approximations. Our method provides a general framework to
obtain optimal results from the physical observables relevant to the problem,
including the external parameterization in time known as scheduling function,
the gauge potential or operator involving the non-adiabatic terms, as well as
the temporal evolution of the energy levels of the system, among others. The
main applications of this methodology have been the and
molecules, represented by a 2-qubit and 4-qubit systems
employing the STO-3G basis. The presented results demonstrate the successful
derivation of a desirable decomposition for the non-adiabatic terms, achieved
through a linear combination utilizing Pauli operators. This attribute confers
significant advantages to its practical implementation within quantum computing
algorithms.Comment: 28 pages, 10 figures, 1 algorithm, 1 tabl
Intelligent flight control systems
The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms
Design, Development and Implementation of Intelligent Algorithms to Increase Autonomy of Quadrotor Unmanned Missions
This thesis presents the development and implementation of intelligent algorithms to increase autonomy of unmanned missions for quadrotor type UAVs. A six-degree-of freedom dynamic model of a quadrotor is developed in Matlab/Simulink in order to support the design of control algorithms previous to real-time implementation. A dynamic inversion based control architecture is developed to minimize nonlinearities and improve robustness when the system is driven outside bounds of nominal design. The design and the implementation of the control laws are described. An immunity-based architecture is introduced for monitoring quadrotor health and its capabilities for detecting abnormal conditions are successfully demonstrated through flight testing. A vision-based navigation scheme is developed to enhance the quadrotor autonomy under GPS denied environments. An optical flow sensor and a laser range finder are used within an Extended Kalman Filter for position estimation and its estimation performance is analyzed by comparing against measurements from a GPS module. Flight testing results are presented where the performances are analyzed, showing a substantial increase of controllability and tracking when the developed algorithms are used under dynamically changing environments. Healthy flights, flights with failures, flight with GPS-denied navigation and post-failure recovery are presented
Unified Behavior Framework for Reactive Robot Control in Real-Time Systems
Endeavors in mobile robotics focus on developing autonomous vehicles that operate in dynamic and uncertain environments. By reducing the need for human-in- the-loop control, unmanned vehicles are utilized to achieve tasks considered dull or dangerous by humans. Because unexpected latency can adversely affect the quality of an autonomous system\u27s operations, which in turn can affect lives and property in the real-world, their ability to detect and handle external events is paramount to providing safe and dependable operation. Behavior-based systems form the basis of autonomous control for many robots. This thesis presents the unified behavior framework, a new and novel approach which incorporates the critical ideas and concepts of the existing reactive controllers in an effort to simplify development without locking the system developer into using any single behavior system. The modular design of the framework is based on modern software engineering principles and only specifies a functional interface for components, leaving the implementation details to the developers. In addition to its use of industry standard techniques in the design of reactive controllers, the unified behavior framework guarantees the responsiveness of routines that are critical to the vehicle\u27s safe operation by allowing individual behaviors to be scheduled by a real-time process controller. The experiments in this thesis demonstrate the ability of the framework to: 1) interchange behavioral components during execution to generate various global behavior attributes; 2) apply genetic programming techniques to automate the discovery of effective structures for a domain that are up to 122 percent better than those crafted by an expert; and 3) leverage real-time scheduling technologies to guarantee the responsiveness of time critical routines regardless of the system\u27s computational load
- …