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Intelligent Control
Intelligent control is a class of control techniques that use various artificial intelligence computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation and genetic algorithms. Overview Intelligent control can be divided into the following major sub-domains: * Neural network control * Machine learning control * Reinforcement learning * Bayesian control * Fuzzy control * Neuro-fuzzy control * Expert Systems * Genetic control New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Neural network controller Neural networks have been used to solve problems in almost all spheres of science and technology. Neural network control basically involves two steps: * System identification * Control It has been shown that a feedforward network with nonlinear, continuous and differentiable activation func ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Control Theory
Control theory is a field of control engineering and applied mathematics that deals with the control system, control of dynamical systems in engineered processes and machines. The objective is to develop a model or algorithm governing the application of system inputs to drive the system to a desired state, while minimizing any ''delay'', ''overshoot'', or ''steady-state error'' and ensuring a level of control Stability theory, stability; often with the aim to achieve a degree of Optimal control, optimality. To do this, a controller with the requisite corrective behavior is required. This controller monitors the controlled process variable (PV), and compares it with the reference or Setpoint (control system), set point (SP). The difference between actual and desired value of the process variable, called the ''error'' signal, or SP-PV error, is applied as feedback to generate a control action to bring the controlled process variable to the same value as the set point. Other aspects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Universal Approximation Theorem
In the mathematical theory of artificial neural networks, universal approximation theorems are theorems of the following form: Given a family of neural networks, for each function f from a certain function space, there exists a sequence of neural networks \phi_1, \phi_2, \dots from the family, such that \phi_n \to f according to some criterion. That is, the family of neural networks is dense in the function space. The most popular version states that feedforward networks with non-polynomial activation functions are dense in the space of continuous functions between two Euclidean spaces, with respect to the compact convergence topology. Universal approximation theorems are existence theorems: They simply state that there ''exists'' such a sequence \phi_1, \phi_2, \dots \to f, and do not provide any way to actually find such a sequence. They also do not guarantee any method, such as backpropagation, might actually find such a sequence. Any method for searching the space of neural ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Hybrid Intelligent System
Hybrid intelligent system denotes a software system which employs, in parallel, a combination of methods and techniques from artificial intelligence subfields, such as: * Neuro-symbolic systems * Neuro-fuzzy systems * Hybrid connectionist-symbolic models * Fuzzy expert systems * Connectionist expert systems * Evolutionary neural networks * Genetic fuzzy systems * Rough fuzzy hybridization * Reinforcement learning with fuzzy, neural, or evolutionary methods as well as symbolic reasoning methods. From the cognitive science perspective, every natural intelligent system is hybrid because it performs mental operations on both the symbolic and subsymbolic levels. For the past few years, there has been an increasing discussion of the importance of A.I. Systems Integration. Based on notions that there have already been created simple and specific AI systems (such as systems for computer vision, speech synthesis, etc., or software that employs some of the models mentioned above) an ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Function Approximation
In general, a function approximation problem asks us to select a function (mathematics), function among a that closely matches ("approximates") a in a task-specific way. The need for function approximations arises in many branches of applied mathematics, and computer science in particular , such as predicting the growth of microbes in microbiology. Function approximations are used where theoretical models are unavailable or hard to compute. One can distinguish two major classes of function approximation problems: First, for known target functions approximation theory is the branch of numerical analysis that investigates how certain known functions (for example, special functions) can be approximated by a specific class of functions (for example, polynomials or rational functions) that often have desirable properties (inexpensive computation, continuity, integral and limit values, etc.). Second, the target function, call it ''g'', may be unknown; instead of an explicit formu ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Artificial Intelligence Systems Integration
The core idea of artificial intelligence systems integration is making individual software components, such as speech synthesizers, interoperable with other components, such as common sense knowledgebases, in order to create larger, broader and more capable A.I. systems. The main methods that have been proposed for integration are message routing, or communication protocols that the software components use to communicate with each other, often through a middleware blackboard system. Most artificial intelligence systems involve some sort of integrated technologies, for example, the integration of speech synthesis technologies with that of speech recognition. However, in recent years, there has been an increasing discussion on the importance of systems integration as a field in its own right. Proponents of this approach are researchers such as Marvin Minsky, Aaron Sloman, Deb Roy, Kristinn R. Thórisson and Michael A. Arbib. A reason for the recent attention A.I. integrati ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Applications Of Artificial Intelligence
Artificial intelligence (AI) has been used in applications throughout industry and academia. In a manner analogous to electricity or computers, AI serves as a general-purpose technology. AI programs are designed to simulate human perception and understanding. These systems are capable of adapting to new information and responding to changing situations. Machine learning has been used for various scientific and commercial purposes including language translation, image recognition, decision-making, credit scoring, and e-commerce. Agriculture In agriculture, AI has been proposed as a way for farmers to identify areas that need irrigation, fertilization, or pesticide treatments to increase yields, thereby improving efficiency. AI has been used to attempt to classify livestock pig call emotions, automate greenhouses, detect diseases and pests, and optimize irrigation. Architecture & Design AI in architecture has created a way for architects to create things beyond human unders ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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AI Effect
The AI effect is the discounting of the behavior of an artificial intelligence program as not "real" intelligence. The author Pamela McCorduck writes: "It's part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, 'that's not thinking'." Researcher Rodney Brooks complains: "Every time we figure out a piece of it, it stops being magical; we say, 'Oh, that's just a computation." Definition "The AI effect" refers to a phenomenon where either the definition of AI or the concept of intelligence is adjusted to exclude capabilities that AI systems have mastered. This often manifests as tasks that AI can now perform successfully no longer being considered part of AI, or as the notion of intelligence itself being redefined to exclude AI achievements. Edward Geist credits John McCarthy for coining the term ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Action Selection
Action selection is a way of characterizing the most basic problem of intelligent systems: what to do next. In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behavior in an agent environment. The term is also sometimes used in ethology or animal behavior. One problem for understanding action selection is determining the level of abstraction used for specifying an "act". At the most basic level of abstraction, an atomic act could be anything from ''contracting a muscle cell'' to ''provoking a war''. Typically for any one action-selection mechanism, the set of possible actions is predefined and fixed. Most researchers working in this field place high demands on their agents: * The acting agent typically must select its action in dynamic and unpredictable environments. * The agents typically act in real time; therefore they must make ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Control Engineering
Control engineering, also known as control systems engineering and, in some European countries, automation engineering, is an engineering discipline that deals with control systems, applying control theory to design equipment and systems with desired behaviors in control environments. The discipline of controls overlaps and is usually taught along with electrical engineering, chemical engineering and mechanical engineering at many institutions around the world. The practice uses sensors and detectors to measure the output performance of the process being controlled; these measurements are used to provide corrective feedback helping to achieve the desired performance. Systems designed to perform without requiring human input are called automatic control systems (such as cruise control for regulating the speed of a car). Multi-disciplinary in nature, control systems engineering activities focus on implementation of control systems mainly derived by mathematical modeling of a diver ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Systems Theory
Systems theory is the Transdisciplinarity, transdisciplinary study of systems, i.e. cohesive groups of interrelated, interdependent components that can be natural or artificial. Every system has causal boundaries, is influenced by its context, defined by its structure, function and role, and expressed through its relations with other systems. A system is "more than the sum of its parts" when it expresses synergy or emergent behavior. Changing one component of a system may affect other components or the whole system. It may be possible to predict these changes in patterns of behavior. For systems that learn and adapt, the growth and the degree of adaptation depend upon how well the system is engaged with its environment and other contexts influencing its organization. Some systems support other systems, maintaining the other system to prevent failure. The goals of systems theory are to model a system's dynamics, Theory of constraints, constraints, conditions, and relations; and to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Particle Filter
Particle filters, also known as sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Pierre Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Jun S. Liu and Rong Chen in 1998. Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of a stochastic process giv ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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Kalman Filter
In statistics and control theory, Kalman filtering (also known as linear quadratic estimation) is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, to produce estimates of unknown variables that tend to be more accurate than those based on a single measurement, by estimating a joint probability distribution over the variables for each time-step. The filter is constructed as a mean squared error minimiser, but an alternative derivation of the filter is also provided showing how the filter relates to maximum likelihood statistics. The filter is named after Rudolf E. Kálmán. Kalman filtering has numerous technological applications. A common application is for guidance, navigation, and control of vehicles, particularly aircraft, spacecraft and ships Dynamic positioning, positioned dynamically. Furthermore, Kalman filtering is much applied in time series analysis tasks such as signal processing and econometrics. K ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |