Transiently Chaotic Neural Network Research Articles

Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People

Emotion analysis is an effective method for improving interaction and understanding for speech-impaired people. We can provide the best interaction and support emotional well-being by analyzing the emotion conveyed through speech. Using deep learning (DL) or machine learning algorithms for training an emotion classification method. This might include training classifiers namely random forests, deep neural networks, or support vector machines. It is noteworthy that emotion analysis could be effective; however, it is crucial to consider individual differences and context while interpreting emotion. Furthermore, ensuring data protection and privacy and obtaining consent are vital features to consider while working with sensitive speech data. Therefore, this study presents an emotion analysis approach using improved cat swarm optimization with machine learning (EA-ICSOML) technique. The EA-ICSOML technique applies the concepts of computer vision and DL to identify various types of emotions. For feature vector generation, the ShuffleNet model is used in this work. To adjust the hyperparameters compared to the ShuffleNet system, the ICSO algorithm is used. Finally, the recognition and classification of emotions are performed using the Transient Chaotic Neural Network approach. The performance validation of the EA-ICSOML technique is validated on facial emotion databases. The simulation result inferred the improved emotion recognition results of the EA-ICSOML approach compared to other recent models in terms of different evaluation measures.

Chaotic Neural Network with Nonlinear Delayed Self-feedback and Applications

We propose a novel transient chaotic neural network model with a nonlinear delayed self-feedback term, aiming to leverage its complex dynamical behavior for algorithm optimization. To avoid the influence of chance, we conduct experiments using randomly generated data and analyze the model’s chaotic dynamical behavior by visualizing the stability of the neurons through inverse bifurcation diagrams and maximum Lyapunov exponent diagrams. The simulation results demonstrate the remarkable effectiveness of this new model in network optimization, with a success rate even reaching 100%, surpassing previous chaotic neural network models. Notably, in the following sections, all nonlinear terms are represented using trigonometric functions.

The Multiple Frequency Conversion Sinusoidal Chaotic Neural Network and Its Application

Aiming at the problem that the global search performance of a transiently chaotic neural network is not ideal, a multiple frequency conversion sinusoidal chaotic neural network (MFCSCNN) model is proposed based on the biological mechanism of the brain, including multiple functional modules and sinusoidal signals of different frequencies. In this model, multiple FCS functions and Sigmoid functions with different phase angles were used to construct the excitation function of neurons in the form of weighted sum. In this paper, the inverted bifurcation diagram, Lyapunov exponential diagram and parameter range of the model are given. The dynamic characteristics of the model are analyzed and applied to function optimization and combinatorial optimization problems. Experimental results show that the multiple frequency conversion sinusoidal chaotic neural network has better global search performance than the transient chaotic neural network and other related models.

A Ferroelectric Memristor-Based Transient Chaotic Neural Network for Solving Combinatorial Optimization Problems

A transient chaotic neural network (TCNN) is particularly useful for solving combinatorial optimization problems, and its hardware implementation based on memristors has attracted great attention recently. Although previously used filamentary memristors could provide the desired nonlinearity for implementing the annealing function of a TCNN, the controllability of filamentary switching still remains relatively poor, thus limiting the performance of a memristor-based TCNN. Here, we propose to use ferroelectric memristor to implement the annealing function of a TCNN. In the ferroelectric memristor, the conductance can be tuned by switching the lattice non-centrosymmetry-induced polarization, which is a nonlinear switching mechanism with high controllability. We first establish a ferroelectric memristor model based on a ferroelectric tunnel junction (FTJ), which exhibits the polarization-modulated tunnel conductance and the nucleation-limited-switching (NLS) behavior. Then, the conductance of the ferroelectric memristor is used as the self-feedback connection weight that can be dynamically adjusted. Based on this, a ferroelectric memristor-based transient chaotic neural network (FM-TCNN) is further constructed and applied to solve the traveling salesman problem (TSP). In 1000 runs for 10-city TSP, the FM-TCNN achieves a shorter average path distance, a 32.8% faster convergence speed, and a 2.44% higher global optimal rate than the TCNN.

Application of the edge of chaos in combinatorial optimization* *Project supported by the National Natural Science Foundation of China (Grant No. 12074335) and the National Science and Technology Major Project of the Ministry of Science and Technology of China (Grant No. 2016YFA0300402).

Many problems in science, engineering and real life are related to the combinatorial optimization. However, many combinatorial optimization problems belong to a class of the NP-hard problems, and their globally optimal solutions are usually difficult to solve. Therefore, great attention has been attracted to the algorithms of searching the globally optimal solution or near-optimal solution for the combinatorial optimization problems. As a typical combinatorial optimization problem, the traveling salesman problem (TSP) often serves as a touchstone for novel approaches. It has been found that natural systems, particularly brain nervous systems, work at the critical region between order and disorder, namely, on the edge of chaos. In this work, an algorithm for the combinatorial optimization problems is proposed based on the neural networks on the edge of chaos (ECNN). The algorithm is then applied to TSPs of 10 cities, 21 cities, 48 cities and 70 cities. The results show that ECNN algorithm has strong ability to drive the networks away from local minimums. Compared with the transiently chaotic neural network (TCNN), the stochastic chaotic neural network (SCNN) algorithms and other optimization algorithms, much higher rates of globally optimal solutions and near-optimal solutions are obtained with ECNN algorithm. To conclude, our algorithm provides an effective way for solving the combinatorial optimization problems.

A memristor-based transient chaotic neural network model and its application

Transient chaotic neural networks (TCNNs) have shown promise in solving optimization problems but still suffer from slow convergence and being difficult to implement in hardware. In this paper, the HP memristor is introduced to a TCNN to develop a memristor-based transient chaotic neural network (MTCNN) model that is highly efficient, converges quickly, and has significant prospects for physical implementation. The proposed MTCNN makes full use of the nonlinearity and memory-related characteristics of memristors, and their conductance values are used as self-feedback connection weights that can be adjusted dynamically according to the annealing algorithm. The MTCNN model was applied to solve combinatorial optimization problems, including the channel assignment problem (CAP) of four cells and the traveling salesman problem (TSP) of 10 cities. In 500 runs, the MTCNN algorithm delivered a 5% higher optimal solution rate than the TCNN algorithm while using only 70% of its number of iterations in the CAP, and achieved a shorter average distance and a 40% higher convergence speed than the TCNN algorithm in the TSP.

Frequency conversion sinusoidal chaotic neural network and its application

The optimization performance of transiently chaotic neural network (TCNN) is affected by various factors such as chaotic characteristic, model parameters, and annealing function, and its capacity of global optimization is limited. It is demonstrated that the non-monotonic activation function can generate richer chaotic characteristic than the monotonic activation function in the TCNN model. Besides, the activation function involving neurobiological mechanism can not only reflect the rich brain activity in brain waves, but also enhance the non-linear dynamic characteristic, which may further improve the global optimization ability. Hence, a novel chaotic neuron model is proposed with the non-monotonic activation function based on the neurobiological mechanisms from the electroencephalogram. The electroencephalogram consists of five brain waves (i.e., , , , , and waves) which are defined by the quality and intensity of brain waves with different frequency bands ranging from 0.5 Hz to 100 Hz. The brain wave with a higher frequency and a lower amplitude represents a more active brain. Researches demonstrate that the five brain waves can be simplified into sinusoidal waves with different frequencies. Hence, a frequency conversion sinusoidal (FCS) function which has the consistent frequency range and features with brain waves is designed based on the above neurobiological mechanisms. Then a novel chaotic neuron model with non-monotonic activation function which is composed of the FCS function and sigmoid function, is proposed for richer chaotic dynamic characteristic. The reversed bifurcation and the Lyapunov exponent of the chaotic neuron are given and the dynamic system is analyzed, indicating that the proposed FCS neuron model owns richer chaotic dynamic characteristic than transiently chaotic neuron model due to its special non-monotonic activation function. Based on the neuron model, a novel transiently-chaotic neural networkfrequency conversion sinusoidal chaotic neural network (FCSCNN) is constructed and the basis of model parameter selection is provided as well. To validate the effectiveness of the proposed model, the FCSCNN is applied to nonlinear function optimization and 10-city, 30-city, 75-city traveling salesman problem. The experimental results show that 1) the FCSCNN has a good performance under the condition of moderate a, smaller cA(0) and 2(0); 2) on the basis of the appropriate model parameters, the FCSCNN has better global optimization ability and optimization accuracy than Hopfield neural network, TCNN, improved-TCNN due to its richer chaotic characteristic in complicated combinational optimization problem, especially in middle and large scale problem.

Transient chaotic neural network-based disjoint multipath routing for mobile ad-hoc networks

Due to mobility of wireless hosts, routing in mobile ad-hoc networks (MANETs) is a challenging task. Multipath routing is employed to provide reliable communication, load balancing, and improving quality of service of MANETs. Multiple paths are selected to be node-disjoint or link-disjoint to improve transmission reliability. However, selecting an optimal disjoint multipath set is an NP-complete problem. Neural networks are powerful tools for a wide variety of combinatorial optimization problems. In this study, a transient chaotic neural network (TCNN) is presented as multipath routing algorithm in MANETs. Each node in the network can be equipped with a neural network, and all the network nodes can be trained and used to obtain optimal or sub-optimal high reliable disjoint paths. This algorithm can find both node-disjoint and link-disjoint paths with no extra overhead. The simulation results show that the proposed method can find the high reliable disjoint path set in MANETs. In this paper, the performance of the proposed algorithm is compared to the shortest path algorithm, disjoint path set selection protocol algorithm, and Hopfield neural network (HNN)-based model. Experimental results show that the disjoint path set reliability of the proposed algorithm is up to 4.5 times more than the shortest path reliability. Also, the proposed algorithm has better performance in both reliability and the number of paths and shows up to 56% improvement in path set reliability and up to 20% improvement in the number of paths in the path set. The proposed TCNN-based algorithm also selects more reliable paths as compared to HNN-based algorithm in less number of iterations.

Invariant Features Tracking Based on Transiently Chaotic Neural Network for Markerless Augmented Reality

Tracking and registration of camera and object is one of the most important issues in Augmented Reality (AR) systems. Markerless visual tracking technologies with image feature are used in many AR applications. Feature point based neural network image matching method has attracted considerable attention in recent years. This paper proposes an approach to feature point correspondence of image sequence based on transient chaotic neural networks. Rotation and scale invariant features are extracted from images firstly, and then transient chaotic neural network is used to perform global feature matching and perform the initialization phase of the tracking. Experimental results demonstrate the efficiency and the effectiveness of the proposed method.

Improvement and Application of Transient Chaos Neural Networks

In order to get better control of the chaotic randomness and improve the convergence rate of Transient Chaotic Neural Networks(TCNN),Effective improvement is proposed in this study.By modifying the Incentive function with composite sine sigmoid function and Time-varying Gain are adopted in the function, and two-stage annealing mechanism is applied in the annealing process of the dynamic equations.Empirical results show that combination of the two aspects has a better control of the two processes of the chaotic state,improved algorithm obtains a wealth of chaotic characteristics. Experiments on the channel allocation problem indicate that the modified algorithm can better solve this kind of Combinatorial optimization problems. Results are compared with other prior algorithms such as basic TCNN and only subsection of TCNN.The comparison shows that the performance of the algorithm has been greatly improved, local optima is avoided, and the convergence speed is improved more than 14%.

Delay-Constrained Multicast Routing Using the Noisy Chaotic Neural Networks

We present a method to compute the delay constrained multicast routing tree by employing chaotic neural networks. Experimental result shows that the noisy chaotic neural network (NCNN) provides optimal solution more often compared to the transiently chaotic neural network (TCNN) and the Hopfield neural network (HNN). Furthermore, compared with the bounded shortest multicast algorithm (BSMA), the noisy chaotic neural network is able to find multicast trees with lower cost.

A TCNN filter algorithm to maximum clique problem

In this paper, coefficient sensitiveness of transiently chaotic neural network (TCNN) for maximum clique problem (MCP) is analyzed. By introducing a new definition of adaptivity into TCNN and utilizing a neuron filter, the domain of coefficient selection is enlarged to search globally optimal solution and near-optimal solution on MCP. Based on our analysis on the characteristic of TCNN, we propose a TCNN filter algorithm to shrink the network scale and improve solution quality. The algorithm effectively relaxes the coefficient sensitiveness and improves network ability to solve MCP. Simulations have been performed to verify the validity of our algorithm.

Transiently chaotic neural networks with piecewise linear output functions

Admitting both transient chaotic phase and convergent phase, the transiently chaotic neural network (TCNN) provides superior performance than the classical networks in solving combinatorial optimization problems. We derive concrete parameter conditions for these two essential dynamic phases of the TCNN with piecewise linear output function. The confirmation for chaotic dynamics of the system results from a successful application of the Marotto theorem which was recently clarified. Numerical simulation on applying the TCNN with piecewise linear output function is carried out to find the optimal solution of a travelling salesman problem. It is demonstrated that the performance is even better than the previous TCNN model with logistic output function.

A method to improve the transiently chaotic neural network

In this article, we propose a method for improving the transiently chaotic neural network (TCNN) by introducing several time-dependent parameters. This method allows the network to have rich chaotic dynamics in its initial stage and to reach a state in which all neurons are stable soon after the last bifurcation. This enables the network to have rich search ability initially and to use less CPU time to reach a stable state. The simulation results on the N-queen problem confirm that this method effectively improves both the solution quality and convergence speed of TCNN.

SIMULATED ANNEALING SCHEMES IN TRANSIENTLY CHAOTIC NEURAL NETWORK MODEL

A transiently chaotic neural network (TCNN) is an approximation method for combinatorial optimization problems. The evolution function of self-back connect weight, called annealing function, influences the accurate and search speed of TCNN model. This paper analyzes two common annealing schemes. Furthermore we proposed a new subsection exponential annealing function. Finally, we compared these annealing schemes in TSP problem.

Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem

Cell formation is a key issue in the design of cellular manufacturing systems. Effective grouping of parts and machines can improve considerably the performance of manufacturing cells. The transiently chaotic neural network (TCNN) is a recent methodology in intelligent computation that has the advantages of both the chaotic neural network and the Hopfield neural network. The present paper investigates the dynamics of the TCNN network and studies the feasibility and robustness of final solutions of TCNN when applied to the cell formation problem. The paper provides insight into the feasibility and robustness of TCNN for cell formation problems. It also discusses how to set the initial values of the TCNN parameters in the case of well-structured and ill-structured cell formation problems.

Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem

Cell formation is a key issue in the design of cellular manufacturing systems. Effective grouping of parts and machines can improve considerably the performance of manufacturing cells. The transiently chaotic neural network (TCNN) is a recent methodology in intelligent computation that has the advantages of both the chaotic neural network and the Hopfield neural network. The present paper investigates the dynamics of the TCNN network and studies the feasibility and robustness of final solutions of TCNN when applied to the cell formation problem. The paper provides insight into the feasibility and robustness of TCNN for cell formation problems. It also discusses how to set the initial values of the TCNN parameters in the case of well-structured and ill-structured cell formation problems.

Critical temperature of the transiently chaotic neural network

The dynamical behaviour of an optimizing neural network is closely related to its parameters. For the transiently chaotic neural network (TCNN), the temperature, i.e., self-feedback weighting, is an important parameter for the network performance. While a high temperature is required to investigate chaotic dynamics, a low temperature is preferred for combinatorial optimization application. In this article, we derived this critical temperature of the TCNN analytically and illustrated its validity using computer simulation.

A study of the transiently chaotic neural network for combinatorial optimization

We consider the dynamic behavior of the transiently chaotic neural network (TCNN). Although its dynamical behavior is of interest in investigating neural dynamics, we observed that the chaotic phase in a TCNN is not a necessary condition for the network to reach the global solution for a combinatorial optimization problem. In fact, the global solution is a result of the time varying terms in a TCNN. We, therefore, generalized the TCNN to a nonautonomous Hopfield neural network (NAHNN). Simulation shows that the NAHNN is very effective in solving the traveling salesman problem (TSP).

Self-Organizing Transient Chaotic Neural Network for Cellular Channel Assignment

This paper presents a self-organizing transient chaotic neural network to solve the channel assignment problem, one of NP-complete problems. The proposed neural network consists of two parts. The first part is the self-organizing evolution stage, which based on the mutual inhibition mechanisms of bristle differentiation and the problem's heuristic information. The second part is the transient chaotic neural network executing stage. A significant property of the TCNN model is that the chaotic neurodynamics is temporarily generated for searching and self-organizing in order to escape the local minima. In the proposed neural network, the first part is used to improve the quality of the obtained solutions. The simulating results have shown that the self-organizing transient chaotic neural network improves greatly performance through solving the well-known benchmark problems, especially for the Sivarajan's and Kunz's benchmark problems, while the performance is comparable with existing algorithms.