Codes in python for training artificial neural network using particle swarm optimization

It represents a population-based adaptive optimization technique that is influenced by several "strategy parameters". Choosing reasonable parameter values for the PSO is crucial for its convergence behavior, and depends on the optimization task.

We present a method for parameter meta-optimization based on PSO and its application to neural network training. We assessed the performance of the OPSO method on a set of five artificial fitness functions and compared it to the performance of two popular PSO implementations.

Our results indicate that PSO performance can be improved if meta-optimized parameter sets are applied. In addition, we could improve optimization speed and quality on the other PSO methods in the majority of our experiments.

We applied the OPSO method to neural network training with the aim to build a quantitative model for predicting blood-brain barrier permeation of small organic molecules.

On average, training time decreased by a factor of four and two in comparison to the other PSO methods, respectively. By applying the OPSO method, a prediction model showing good correlation with training- test- and validation data was obtained.

Optimizing the free parameters of the PSO method can result in performance gain. The OPSO approach yields parameter combinations improving overall optimization performance. Its conceptual simplicity makes implementing the method a straightforward task. Optimizing parameters of multivariate systems is a general problem in computational biology.

One of the many methods developed for parameter optimization is Particle Swarm Optimization PSOwhich was introduced by Kennedy and Eberhart in [ 12 ].

Artificial neural networks

Emerging from simulations of dynamic systems such as bird flocks and fish swarms, the original algorithm is grounded on a stochastic search in multimodal search space. The idea of PSO is to have a swarm of particles "flying" through a multidimensional search space, looking for the global optimum. By exchanging information the particles can influence each others' movements. Each particle retains an individual or "cognitive" memory of the best position it has visited, as well as a global or "social" memory of the best position visited by all particles in the swarm.

A particle calculates its next position based on a combination of its last movement vector, the individual and global memories, and a random component. An advantage of PSO is its ability to handle optimization problems with multiple local optima reasonably well and its simplicity of implementation — especially in comparison to related strategies like genetic algorithms GA.

In the field of cheminformatics, PSO has successfully been applied to Quantitative Structure-Activity Relationship QSAR modeling, including k -nearest neighbor and kernel regression [ 3 ], minimum spanning tree for piecewise modeling [ 4 ], partial least squares modeling [ 5 ], and neural network training [ 6 ]. Ever since its capability to solve global optimization problems was discovered, the PSO paradigm has been developed further and improved and several variations of the original algorithm have been proposed.

The PSO algorithm itself contains some parameters which have been shown to affect its performance and convergence behavior [ 10 - 12 ]. Finding an optimal set of PSO parameter values is an optimization problem by itself, and thus can be dealt with by classic optimization techniques.

One approach that has been pursued was based on testing various parameter combinations empirically to find one parameter set which enables PSO to handle all kinds of optimization problems reasonably well [ 11 ]. They showed on a suite of test functions that their composite PSO could surpass the success rate of the plain PSO they were comparing to.

They also tried to have the PSO heuristics optimized by another PSO running in parallel, but were not satisfied with preliminary results and discarded this concept in favor of the DE algorithm [ 13 ].In this study, a new approach to the palmprint recognition phase is presented. After Gabor filtering, standard deviations are computed in order to generate the palmprint feature vector.

Genetic Algorithm-based feature selection is used to select the best feature subset from the palmprint feature set. An Artificial Neural Network ANN based on hybrid algorithm combining Particle Swarm Optimization PSO algorithm with back-propagation algorithms has been applied to the selected feature vectors for recognition of the persons.

This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. Int J Neural Syst 17 5 — Neural Comput Appl 1— Nanni L, Lumini A A multi-matcher system based on knuckle-based features. Neural Comput Appl 18 1 — Malcangi M Soft-computing methods for robust authentication using soft-biometric data.

Pattern Recognit — Kong WK, Zhang D Palmprint texture analysis based on low-resolution images for personal authentication. Pattern Anal Appl 92—— Pattern Recognit Lett 24 15 — Image Vis Comput 23 5 — Inform Sci 8 — Pattern Anal Appl 7 3 — Zhang DD Palmprint authentication: international series on biometrics.

Springer, Berlin. Accessed 14 Mar Pattern Anal Appl 10 2 — Theodoridis S, Koutroumbas K Pattern recognition.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I want to train a neural network using Particle Swarm Optimization algorithm, but matlab toolbox doesn't have any function for train network with this algorithm, I've searched and founded some PSO toolboxes but they didn't work.

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Can anybody help me please? Learn more. Training neural network using particle swarm optimization in matlab Ask Question.

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Training a neural network using particle swarm optimization

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codes in python for training artificial neural network using particle swarm optimization

The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information.

I want to train a neural network using Particle Swarm Optimization algorithm, but matlab toolbox doesn't have any function for train network with this algorithm, I've searched and founded some PSO toolboxes but they didn't work.

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Can anybody help me please? Learn more.

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Training neural network using particle swarm optimization in matlab Ask Question. Asked 6 years, 2 months ago. Active 5 years, 11 months ago. Viewed 3k times. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home? Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. With Regular Neural Networks you just hope that you hadn't chosen the second network. If iterations took 20 days.

codes in python for training artificial neural network using particle swarm optimization

Even after 20 days are you really sure that you got the best optimum loss and would further training improve network performance. Thus we propose a new hybrid approach one that scales up crazy parallely. Particle Swarm Optimization with Neural Networks.

Not that a great idea. Current testing based on fully connected networks, training with only PSO isn't a sufficient. Works great for Initializations. Increasing hidden layers however makes pso converge quicker.

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An increase in network size is compensated with lesser number of iterations. Way to go. Faster than traditional approaches if you have sufficient power. Robust against Local Optimas. With the 32 particles the sample network can be trained under 20K iteration with a final loss of 1. Oops did we mention the learning rate 0. And yes it never gets stuck up on local minimas.

A much bigger search space is explored by the particles as compared to a single particle.This is a simple implementation of a neural network trained using Particle Swarm Optimization in order to solve the two-spiral problem.

The and activation functions were used for the input-hidden and hidden-output layers respectively Sopena, Romero, et al. The cross-entropy error was used as the cost function.

The two-spiral problem is a particularly difficult problem that requires separating two logistic spirals from one another Lang and Witbrock, Figure 1: Graph of the Two-Spiral Problem. Particle swarm optimization is a population-based search algorithm that is based on the social behavior of birds within a flock Engelbrecht, In PSO, the particles are scattered throughout the hyperdimensional search space. These particles use the results found by the others in order to build a better solution.

For Particle Swarm Optimization, we first initialize the swarm, and then observe the effects of the social and cognitive parameters to its performance. After that, parameter tuning will be implemented as the neural network will be cross-validated with respect to the social and cognitive parameters. For any particleits position at time-step with respect to the ANN parameters are expressed as:. The particle matrix P was initialized using uniform distribution.

This is to maximize the exploration capability of the particles over the search space by distributing it evenly. Here, I swept over different values for the social and cognitive components, and came up with this matrix:. Figure 2: Heat map for testing the social and cognitive parameters. From this value matrix, it is clear that accuracy may improve with values where the ratio of and is.

The following images below show the simulation of the swarm only at parameters and given different values of the social and cognitive components. Below these simulations, a graph of the cost is traced.

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Figure 2: Swarm behavior at when it is fully social left and fully cognitive right. Figure 3: Graph of the cost per iteration. For a swarm that has no cognitive parameter, the particles tend to move to the global best quite early, resulting to a curve that converges easily.

On the other hand, for a swarm that has no social parameter, the particles tend to move only to their personal bests, without doing an effort to reach the best score found by the whole swarm. The best score that was achieved using this optimization algorithm is The figure below shows the generalization ability that the algorithm can attain. Moreover, the table below shows the values I set for the parameters. One of the main advantage of PSO is that there are only at a minimum two parameters to control.

Balancing the tradeoff between exploitation and exploration is much easier as compared to other algorithms because it is much more intuitive. However, initialization is also important in PSO. It matters how the particles are distributed along the search space as it may increase or decrease their chances of finding the optimal solution.

In this implementation, a small value of was used for the spread parameter.

Project: Neural Network training with PSO, CODE in details. Part: 8/10

Compared with the benchmark solution, the PSO has a tendency to run slower. Moreover, as the number of particles increase, the computational time increases because the size of the matrices to be computed also expands. You can access the gist here. Figure 1: Graph of the Two-Spiral Problem Particle Swarm Optimization Particle swarm optimization is a population-based search algorithm that is based on the social behavior of birds within a flock Engelbrecht, Methodology For Particle Swarm Optimization, we first initialize the swarm, and then observe the effects of the social and cognitive parameters to its performance.To browse Academia.

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codes in python for training artificial neural network using particle swarm optimization

West Bengal, India. Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced.

The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories.

In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm. Keywords - Artificial neural network, particle swarm optimization, machine learning, back-propagation, IRIS.

Social optimization occurs in the time frame of ordinary experience - in fact, it is ordinary experience. In addition to its ties with A-life, particle swarm optimization has obvious ties with evolutionary computation. Conceptually, it seems to lie somewhere between genetic algorithms and evolutionary programming. Here we describe the use of back propagation neural networks BPNN towards the identification of iris plants on the basis of the following measurements: sepal length, sepal width, petal length, and petal width.

There is a comparison of the fitness of neural networks with input data normalized by column, row, sigmoid, and column constrained sigmoid normalization.

Also contained within the paper is an analysis of the performance results of back propagation neural networks with various numbers of hidden layer neurons, and differing number of cycles epochs. The analysis of the performance of the neural networks is based on several criteria: incorrectly identified plants by training set recall and testing set accuracyspecific error within incorrectly identified plants, overall data set error as tested, and class identification precision.

Feed-forward ANNs are commonly used for function approximation and pattern classifications. Back-propagation algorithm and its variations such as QuickProp [11] and RProp [12] are likely to reach local minima especially in case that the error surface is rugged. In addition, the efficiency of BP methods depends on the selection of appropriate learning parameters.

Artificial Intelligence : A precise definition of intelligence is unavailable. It is probably explained best by discussing some of the aspects. In general, intelligence has something to do with the process of knowledge and thinking, also called cognition. These mental processes are needed for, i. One needs to possess a certain intelligence to be able to do these tasks. Not only the deliberate thought processes are part of cognition, also the unconscious processes like perceiving and recognizing an object belong to it.

However, they eventually they reach the best location of food source by means of communicating with each other.

A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. In most cases a neural network is an adaptive system that changes its structure during a learning phase. Neural networks are used to model complex relationships between inputs and outputs or to find patterns in data. Similar to the GA, the PSO algorithm is a global algorithm, which has a strong ability to find global optimistic result, this PSO algorithm, The BP algorithm, on the contrary, has a strong ability to find local optimistic result, but its ability to find the global optimistic result is weak.

By combining the PSO with the BP, The fundamental idea for this hybrid algorithm is that at the beginning stage of searching for the optimum, the PSO is employed to accelerate the training speed.

When the fitness function value has not changed for some generations, or value changed is smaller than a predefined number, the searching process is switched to gradient descending searching according to this heuristic knowledge. First, all the particles are updated according to the Equations. Until a new generation set of particles are generated, and then those new particles are used to search the global best position in the solution space. Finally the BP algorithm is used to search around the global optimum.


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