Exact Solutions of Time-Fractional KdV Equations by Using
... newly submitted to literature to construct exact solutions of nonlinear fractional differential equations. According to these data, we can deduce that GKM plays a crucial role to reach analytical solutions of nonlinear fractional differential equations. Additionally, we note that this method is high ...
... newly submitted to literature to construct exact solutions of nonlinear fractional differential equations. According to these data, we can deduce that GKM plays a crucial role to reach analytical solutions of nonlinear fractional differential equations. Additionally, we note that this method is high ...
A fast Newton`s method for a nonsymmetric - Poisson
... is quite natural to aim to design algorithms which exploit the structure of the matrices and thus have a cost of order lower than O(n3 ) ops. A step in this direction has been done by L.-Z. Lu [16] who has designed a vector iteration whose limit allows one to easily recover the solution. The iterati ...
... is quite natural to aim to design algorithms which exploit the structure of the matrices and thus have a cost of order lower than O(n3 ) ops. A step in this direction has been done by L.-Z. Lu [16] who has designed a vector iteration whose limit allows one to easily recover the solution. The iterati ...
rca icml
... A key observation is that in many unsupervised learning tasks, such groups of similar points may be extracted from the data with minimal effort and possibly automatically, without the need for labels. This occurs when the data originates from a natural sequence that can be modeled as a Markovian pro ...
... A key observation is that in many unsupervised learning tasks, such groups of similar points may be extracted from the data with minimal effort and possibly automatically, without the need for labels. This occurs when the data originates from a natural sequence that can be modeled as a Markovian pro ...
幻灯片 1 - Center for High Energy Physics, Tsinghua University
... second order phase transition second order phase transition ...
... second order phase transition second order phase transition ...
Business Process Innovation with Artificial Intelligence
... state of the world and the future (because the outcome of actions is uncertain) and where agents have to deal with conflicting goals, for example when they want to maximize the profitability while minimizing risks, see [22, 19] for two pointers to seminal early publications. Decision theory allows a ...
... state of the world and the future (because the outcome of actions is uncertain) and where agents have to deal with conflicting goals, for example when they want to maximize the profitability while minimizing risks, see [22, 19] for two pointers to seminal early publications. Decision theory allows a ...
Conservation decision-making in large state spaces
... applicability has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small proble ...
... applicability has always been limited by the so-called curse of dimensionality. The curse of dimensionality is the problem that adding new state variables inevitably results in much larger (often exponential) increases in the size of the state space, which can make solving superficially small proble ...
Genetic Basis and Improvement of Reproductive Traits
... increased calf and heifer mortality, slower re-breeding performance and considerable additional labour and veterinary expense. Therefore, improving these kinds of traits is important for dairy cattle breeders. In meat sheep production, litter size and days to lambing are two of the most important tr ...
... increased calf and heifer mortality, slower re-breeding performance and considerable additional labour and veterinary expense. Therefore, improving these kinds of traits is important for dairy cattle breeders. In meat sheep production, litter size and days to lambing are two of the most important tr ...
Hardware: Input, Processing, and Output Devices
... Build models of the real world Use models to make predictions Genetic Algorithms: Typically uses an existing model (Fitness Function) Searches for a good (or optimal) solution to the model. ...
... Build models of the real world Use models to make predictions Genetic Algorithms: Typically uses an existing model (Fitness Function) Searches for a good (or optimal) solution to the model. ...
slides
... • Intensification: giving priority to attributes of a set of elite solutions (usually in weighted probability manner) • Diversification: Discouraging attributes of elite solutions in selection functions in order to diversify the search to other areas of solution space; ...
... • Intensification: giving priority to attributes of a set of elite solutions (usually in weighted probability manner) • Diversification: Discouraging attributes of elite solutions in selection functions in order to diversify the search to other areas of solution space; ...
Particle Swarm Optimization based Maximum Power Point Tracking
... The photovoltaic (PV) power generation has shown a significant potential in supplying renewable and environmental friendly energy. The recent report of REN21 showed the global operation of solar power generation had increased from 139 GW in year 2013 to 177 GW in year 2014 or equivalent to 27.3 % in ...
... The photovoltaic (PV) power generation has shown a significant potential in supplying renewable and environmental friendly energy. The recent report of REN21 showed the global operation of solar power generation had increased from 139 GW in year 2013 to 177 GW in year 2014 or equivalent to 27.3 % in ...
Reinforcement Learning for Neural Networks using Swarm Intelligence
... algorithms [21] have solved many variations of the pole balance problem. A double CMAC network [22] with one trained for generality and the other trained for accuracy near the target was also applied to the double pole balance problem. The neuroevolutionary method Enforced Subpopulations (ESP) [23] ...
... algorithms [21] have solved many variations of the pole balance problem. A double CMAC network [22] with one trained for generality and the other trained for accuracy near the target was also applied to the double pole balance problem. The neuroevolutionary method Enforced Subpopulations (ESP) [23] ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.