Menekan Tingkat Ber Pada Sistem Komunikasi Direct-Sequence CDMA(DS-CDMA) Menggunakan Jaringan Syaraf Tiruan Transient Chaos

Sistem penerima konvensional pada sistem komunikasi DS-CDMA, terjadi degradasi kinerja akibat perbedaan daya dengan sinyal penginterferensi yang tinggi (Nearto- Far) dan nilai korelasi silang kode user yang berbeda tidak nol, yang mengakibatkan Multiple Access Interference (MAI). Jaringan Saraf Tiruan Transient Chaos (TCNN), sangat potensial untuk mengatasi permasalahan MAI dan Near-to-Far pada penerima konvensional DS-CDMA. Sistem penerima Jaringan Saraf Tiruan Transient Chaos (TCNN) dapat diturunkan dengan memanfaatkan fungsi Likelihood. Dengan fungsi Likelihood tersebut akan diperoleh fungsi energi atau fungsi cost dari sistem penerima multiuser DS-CDMA. Fungsi energi sistem penerima multiuser DS-CDMA diselesaikan dengan algoritma TCNN. Pengujian dilakukan dengan simulasi komputer untuk membandingkan kinerja penerima TCNN dengan konvensional. Hasil simulasi, dapat dilihat bahwa sistem penerima Jaringan Saraf Tiruan Transient Chaos dapat memberikan perbaikan kinerja dibandingkan sistem penerima konvensional (Mathced Filter). Perbaikan kinerja penerima TCNN sebesar 85.092 % pada kondisi E N dB o 6 1 / = , Near-to-Far E E 6dB 2 1 / = , tetapi memerlukan tambahan waktu 0.4845 sekon-per-iterasi algoritma TCNN

Shannon Wavelet Chaotic Neural Network with Nonlinear Self-feedback

Shannon wavelet chaotic neural network is a kind of chaotic neural network with non-monotonous activation function composed by Sigmoid and Wavelet. In this paper, wavelet chaotic neural network models with different nonlinear self-feedbacks are proposed and the effects of the different self-feedbacks on simulated annealing are analyzed respectively. Then the proposed models are applied to the 10-city traveling salesman problem (TSP) and by comparison the performance of the model with wavelet self-feedback is superior to that of the rest others presented in this paper. Moreover, the performance of the model with wavelet self-feedback is improved by the scale index and the location index of the wavelet. Finally, the dynamics of an internal state of the model for the 10-city TSP is researched, including chaotic area distribution, the largest Lyapunov exponents and the effects of the chaotic distribution on the performance of the network for 10-city TSP. The numerical simulations show that the models can converge to the global minimum or approximate solutions more efficiently than the Hopfield network, and the performance of the model with wavelet self-feedback is superior to that of the others

Chaotic particle swarm optimization

Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence

Chaotic particle swarm optimization with neural network structure and its application

Abstract: A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network (HNN) structure is proposed. Particles exhibit chaotic behaviour before converging to a stable fixed point which is determined by the best points found by the individual particles and the swarm. During the evolutionary process, the chaotic search expands the search space of individual particles. Using a chaotic system to determine particle weights helps the PSO to escape from the local extreme and find the global optimum. The algorithm is applied to some benchmark problems and a pressure vessel problem with nonlinear constraints. The results show that the proposed algorithm consistently outperforms rival algorithms by enhancing search efficiency and improving search qualit

Menekan Tingkat Ber Pada Sistem Komunikasi Direct-Sequence CDMA(DS-CDMA) Menggunakan Jaringan Syaraf Tiruan Transient Chaos

Sistem penerima konvensional pada sistem komunikasi DS-CDMA, terjadi degradasi kinerja akibat perbedaan daya dengan sinyal penginterferensi yang tinggi (Nearto-Far) dan nilai korelasi silang kode user yang berbeda tidak nol, yang mengakibatkan Multiple Access Interference (MAI). Jaringan Saraf Tiruan Transient Chaos (TCNN), sangat potensial untuk mengatasi permasalahan MAI dan Near-to-Far pada penerima konvensional DS-CDMA. Sistem penerima Jaringan Saraf Tiruan Transient Chaos (TCNN) dapat diturunkan dengan memanfaatkan fungsi Likelihood. Dengan fungsi Likelihood tersebut akan diperoleh fungsi energi atau fungsi cost dari sistem penerima multiuser DS-CDMA. Fungsi energi sistem penerima multiuser DS-CDMA diselesaikan dengan algoritma TCNN. Pengujian dilakukan dengan simulasi komputer untuk membandingkan kinerja penerima TCNN dengan konvensional. Hasil simulasi, dapat dilihat bahwa sistem penerima Jaringan Saraf Tiruan Transient Chaos dapat memberikan perbaikan kinerja dibandingkan sistem penerima konvensional (Mathced Filter). Perbaikan kinerja penerima TCNN sebesar 85.092 % pada kondisi  E1/No = 6dB , Near-to-Far  E2/E1 = 6dB , tetapi memerlukan tambahan waktu  0.4845 sekon-per-iterasi algoritma TCNN. Kata Kunci— Likelihood, Matched Filter, Multiple Access Interference, Near-to-Fa

Routing Optimization of Electric Vehicles for Charging With Event-Driven Pricing Strategy

With the increasing market penetration of electric vehicles (EVs), the charging behavior and driving characteristics of EVs have an increasing impact on the operation of power grids and traffic networks. Existing research on EV routing planning and charging navigation strategies mainly focuses on vehicle-road-network interactions, but the vehicle-to-vehicle interaction has rarely been considered, particularly in studying simultaneous charging requests. To investigate the interaction of multiple vehicles in routing planning and charging, a routing optimization of EVs for charging with an event-driven pricing strategy is proposed. The urban area of a city is taken as a case for numerical simulation, which demonstrates that the proposed strategy can not only alleviate the long-time queuing for EV fast charging but also improve the utilization rate of charging infrastructures. Note to Practitioners - This article was inspired by the concerns of difficulties for electric vehicle (EV)'s fast charging and the imbalance of the utilization rate of charging facilities. Existing route optimization and charging navigation research are mainly applicable to static traffic networks, which cannot dynamically adjust driving routes and charging strategies with real-time traffic information. Besides, the mutual impact between vehicles is rarely considered in these works in routing planning. To resolve the shortcomings of existing models, a receding-horizon-based strategy that can be applied to dynamic traffic networks is proposed. In this article, various factors that the user is concerned about within the course of driving are converted into driving costs, through which each road section of traffic networks is assigned the corresponding values. Combined with the graph theory analysis method, the mathematical form of the dynamic traffic network is presented. Then, the article carefully plans and adjusts EV driving routes and charging strategies. Numerical results demonstrate that the proposed method can significantly increase the adoption of EV fast charging while alleviating unreasonable distributions of regional charging demand.</p

Chaotic Hopfield Neural Network Swarm Optimization and Its Application

A new neural network based optimization algorithm is proposed. The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. The proposed algorithm combines the advantages of traditional PSO, chaos and Hopfield neural networks: particles learn from their own experience and the experiences of surrounding particles, their search behavior is ergodic, and convergence of the swarm is guaranteed. The effectiveness of the proposed approach is demonstrated using simulations and typical optimization problems

Implementation of a New Sigmoid Function in Backpropagation Neural Networks.

This thesis presents the use of a new sigmoid activation function in backpropagation artificial neural networks (ANNs). ANNs using conventional activation functions may generalize poorly when trained on a set which includes quirky, mislabeled, unbalanced, or otherwise complicated data. This new activation function is an attempt to improve generalization and reduce overtraining on mislabeled or irrelevant data by restricting training when inputs to the hidden neurons are sufficiently small. This activation function includes a flattened, low-training region which grows or shrinks during back-propagation to ensure a desired proportion of inputs inside the low-training region. With a desired low-training proportion of 0, this activation function reduces to a standard sigmoidal curve. A network with the new activation function implemented in the hidden layer is trained on benchmark data sets and compared with the standard activation function in an attempt to improve area under the curve for the receiver operating characteristic in biological and other classification tasks

Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality