scholarly journals Applying Neural Networks to Find the Minimum Cost Coverage of a Boolean Function

VLSI Design ◽  
1995 ◽  
Vol 3 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Pong P. Chu
Keyword(s):  
Neural Network ◽  
Boolean Function ◽  
Input Data ◽  
Selection Process ◽  
Optimal Solution ◽  
Minimum Cost ◽  
Hopfield Network ◽  
Neural Network Approach ◽  
Complete Problem ◽  
Np Complete

To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an “energy function” and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.

scholarly journals NLocalSAT: Boosting Local Search with Solution Prediction

2020 ◽  
Author(s):  
Wenjie Zhang ◽  
Zeyu Sun ◽  
Qihao Zhu ◽  
Ge Li ◽  
Shaowei Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Computer Science ◽  
Boolean Satisfiability ◽  
Stochastic Local Search ◽  
Boolean Satisfiability Problem ◽  
Complete Problem ◽  
Random Manner ◽  
Np Complete

The Boolean satisfiability problem (SAT) is a famous NP-complete problem in computer science. An effective way for solving a satisfiable SAT problem is the stochastic local search (SLS). However, in this method, the initialization is assigned in a random manner, which impacts the effectiveness of SLS solvers. To address this problem, we propose NLocalSAT. NLocalSAT combines SLS with a solution prediction model, which boosts SLS by changing initialization assignments with a neural network. We evaluated NLocalSAT on five SLS solvers (CCAnr, Sparrow, CPSparrow, YalSAT, and probSAT) with instances in the random track of SAT Competition 2018. The experimental results show that solvers with NLocalSAT achieve 27% ~ 62% improvement over the original SLS solvers.

Review on Multiobjective Task Scheduling in Cloud Computing using Nature Inspired Algorithms

2018 ◽  
Vol 6 (8) ◽  
pp. 282
Author(s):  
Shikha Chaudhary ◽  
Saroj Hiranwal ◽  
C. P. Gupta
Keyword(s):  
Cloud Computing ◽  
Optimal Solution ◽  
Computing System ◽  
Ant Colony Optimization Algorithm ◽  
New Class ◽  
Complete Problem ◽  
Nature Inspired Algorithm ◽  
Cloud Computing System ◽  
Np Complete ◽  
Nature Inspired Algorithms

In cloud computing huge pool of resources are available and shared through internet. The scheduling is a core technique which determines the performance of a cloud computing system. The goal of scheduling is to allocate task to appropriate machine to achieve one or more QOS. To find the suitable resource among pool of resources to achieve the goal is an NP Complete problem. A new class of algorithm called nature inspired algorithm came into existence to find optimal solution.  In this paper we provide a survey as well as a comparative analysis of various existing nature inspired scheduling algorithms which are based on genetic algorithm and ant colony optimization algorithm. 

A NEURAL NETWORK IMPLEMENTATION FOR THE TRAFFIC CONTROL PROBLEM ON CROSSBAR SWITCH NETWORKS

1992 ◽  
Vol 03 (02) ◽  
pp. 209-218 ◽  
Author(s):  
K.T. Sun ◽  
H.C. Fu
Keyword(s):  
Neural Network ◽  
Control Problem ◽  
Energy Function ◽  
Traffic Control ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Stable State ◽  
Optimal Solution ◽  
Neural Network Approach ◽  
Crossbar Switch

In this paper, we propose a neural network for the traffic control problem on crossbar switch networks. First, we represent this problem by an energy function, then apply the proposed neural network to update the state of the energy function until a stable state is reached. Within O(n) iteration steps, where n is the size of an n×n network, the energy function reaches a stable state which corresponds to a feasible solution of the traffic control problem. Also, the simulation results show that our neural network generates either optimal or near optimal solutions. Based on our neural network approach, many problems of applying neural networks to optimization problems are overcome, for example, the unpredictable converging time to reach a stable state, the probability of converging to a local minimum which corresponds to an invalid solution and the selecting of proper parameters of an energy function for obtaining a good (near optimal) solution, etc.

AN ANNEALED CHAOTIC MAXIMUM NEURAL NETWORK FOR BIPARTITE SUBGRAPH PROBLEM

2004 ◽  
Vol 14 (02) ◽  
pp. 107-116 ◽  
Author(s):  
JIAHAI WANG ◽  
ZHENG TANG ◽  
RONGLONG WANG
Keyword(s):  
Neural Network ◽  
Parallel Algorithm ◽  
Chaotic Dynamics ◽  
Neural Model ◽  
Steepest Descent Method ◽  
Stable Equilibrium Point ◽  
Local Minima ◽  
Complete Problem ◽  
Bipartite Subgraph ◽  
Np Complete

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP–complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP–complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Borehole electrical resistivity modeling using neural networks

Geophysics ◽  
2002 ◽  
Vol 67 (6) ◽  
pp. 1790-1797 ◽  
Author(s):  
Lin Zhang ◽  
Mary M. Poulton ◽  
Tsili Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Input Data ◽  
Window Length ◽  
Neural Network Approach ◽  
Earth Resistivity ◽  
Training Samples ◽  
Resistivity Model ◽  
Dual Laterolog ◽  
Hidden Nodes

A neural network approach has been applied to model downhole resistivity tools, i.e., to generate a synthetic tool response for a given earth resistivity model. The microlaterolog (MLL), shallow dual laterolog (DLLs), and deep dual laterolog (DLLd) tools are modeled using neural networks to demonstrate this approach. Efforts have been made to select various neural network parameters, including the type of neural network, the length of input data for training, the number of hidden nodes, and the number of training samples. A modular neural network (MNN) has been selected because it can facilitate the training and prediction of tool responses in formations with large resistivity variations. The input data for training are taken to be the model formation resistivity values sampled over a depth window. The window length is chosen based on the tool lengths. Three different window lengths are used for experiments: 6.1, 9.1, and 30.5 m. We found the longer window lengths generally have higher modeling accuracy for the three different types of logging tools. The number of hidden nodes needed to yield satisfactory training and prediction data varies from 8 to 25, depending on the type of tool and the window length. Up to 30 000 training samples have been collected to train the MNN. Our modeling examples show that the trained MNN can achieve about 90% accuracy for the MLL log response and about 83% accuracy for the DLLs and DLLd responses. The modeling errors can be described roughly with a Gaussian distribution.

scholarly journals ROUTING OF VEHICLES USING THE METAHEURISTIC ALGORITHM

2020 ◽  
Vol 72 (4) ◽  
pp. 225-230
Author(s):  
D. Nurserik ◽  
◽  
F.R. Gusmanova ◽  
G.А. Abdulkarimova ◽  
K.S. Dalbekova ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Input Data ◽  
Optimal Solution ◽  
Combinatorial Problem ◽  
Routing Problem ◽  
Metaheuristic Methods ◽  
Short Period ◽  
Np Complete

The main goal of the proposed research is to solve the problem of vehicle routing using genetic algorithms. Vehicle Routing Problem (VRP) is an NP-complete complex combinatorial problem. With a large amount of input data in a VRP problem, it is very expensive to find the most optimal solution. Genetic algorithms offer the most optimal solution in a short period of time. This article discusses genetic algorithms based on the mechanism of evolution for finding the optimal route by metaheuristic methods. The aim of the work is to minimize the time needed to find the most acceptable optimal solution to the problem, as well as to develop metaheuristic methods.

scholarly journals ENN-Ensemble based Neural Network Method for Diabetes Classification

2020 ◽  
Vol 9 (3) ◽  
pp. 574-579
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Selection Process ◽  
Hybrid Approach ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Machine Learning Techniques ◽  
Feature Subset ◽  
Neural Network Approach ◽  
Prediction Of Diabetes

Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENNEnsemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

OPTIMAL COMPETITIVE HOPFIELD NETWORK WITH STOCHASTIC DYNAMICS FOR MAXIMUM CUT PROBLEM

2004 ◽  
Vol 14 (04) ◽  
pp. 257-265 ◽  
Author(s):  
JIAHAI WANG ◽  
ZHENG TANG ◽  
QIPING CAO ◽  
RONGLONG WANG
Keyword(s):  
Communication Networks ◽  
Stochastic Dynamics ◽  
Undirected Graph ◽  
Vlsi Circuits ◽  
Hopfield Network ◽  
Local Minima ◽  
Complete Problem ◽  
Maximum Cut ◽  
Np Complete ◽  
Better Than

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm.

Sign in / Sign up

Export Citation Format

Share Document