My question is: can we use neural network to solve optimization problems without training data. For example, I have 2 inputs and 2 outputs network and outputs are used to calculate in another function in the pu
These results are consistent with recent empirical and theoretical work arguing that local minima are not a significant problem for training large neural networks. 通过实验找了一个随机点和参数点的线性空间中loss的值,没有明显上升,所以局部最优点是稀疏的。(有点太随机了,说服力不够)...
Metrics Abstract Neural network (NN) has been tentatively combined into multi-objective genetic algorithms (MOGAs) to solve the optimization problems in physics. However, the computationally complex physical evaluations and limited computing resources always cause the unsatisfied size of training set, which...
The performance of a mean field theory (MFT) neural network technique for finding approximate solutions to optimization problems is investigated for the case of the minimum cut graph bisection problem, which is NP-complete. We address the issues of solution quality, programming complexity, convergence...
粒子群优化神经网络0/1优化问题A hybrid PSO algorithm was proposed, where the Hopfield manpower neural network with better local searching ability was combined with PSO for solving a class of 0/1 knapsack problem. The current global optimum chromosome activated the neural network and obtained a ...
For a general optimization problem, if the objective function is bounded below and its gradient is Lipschitz continuous, we prove that (a) any trajectory of the gradient-based neural network converges to an equilibrium point, and (b) the Lyapunov stability is equivalent to the asymptotical ...
However, this is due to the freezing phenomena in the quantum annealer, which is a particular problem in the quantum device. The resultant solutions are closely related to low-energy states with a certain value of quantum fluctuation as pointed out in the literature42. In other words, the ...
Those scalars are normalized (for instance with a softmax function) and used to define a weighted sum of the representations of elements in the set that can, in turn, be used in the neural network making the query. This form of content-based soft attention was introduced by Bahdanau, Cho...
letpop_size =20u32;// population size.letproblem_dim =10u32;// number of optimization parameters.letproblem =RosenbrockProblem{};// objective function.letgen_count =10u32;// generations number.letsettings =GASettings::new(pop_size,gen_count,problem_dim);letmutga:GA<RosenbrockProblem>=GA:...
As a black-box problem of neural network, building an appropriate model may also be tricky [55]. Show abstract Parameters extraction of photovoltaic models using enhanced generalized normal distribution optimization with neighborhood search 2024, Neural Computing and Applications An improved bearing fault...