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Channel equalization is an important field of adaptive signal processing.When significant noise is added to the transmitted signal in the channel, the received signal at each instant can be considered as a nonlinear function of the past values of transmitted signal .The overall channel response becomes a non-linear dynamic mapping problem .Hence, the channel needs to be equalized using best of the non-linear approximators .In this paper, Functional Link Artificial Neural Network is used as equalizer by training with Hybrid GA-PSO Algorithm as the Least Mean Square (LMS) methodology is not being able to meet the requirements under noisy conditions. From the simulations and results it can be seen that proposed Hybrid GA-PSO training methodology can be considered as a better training algorithm compared to previously proposed GA and PSO trained FLANN.With the increase in the demand of internet technology and multimedia applications, there has been a rise in the necessity of the development of reliable and efficient transmission mechanisms over digital communication channels. Communication channel refers to either a physical transmission medium such as a wire, or a logical connection over a multiplexed medium [1].The transmission of the digital signal is usually carried out over band limited channels for efficient spectrum utilization. However the information transmitted from the transmitter in the form of signals is practically not the same as the one received by the receiver due to channel impairments like inter signal interference (ISI)[2], noise,etc. Noise added in the medium reduces the efficiency of the communication .The process of compensating the effect of the physical channel between the transmitter and the receiver is known as CHANNEL EQUALISATION [2] [3] . It is an important area in the field of communication as it can greatly improve the quality of transmission which in turn results in better efficiency. A typical communication channel equalizer has been shown below:

This paper presents a hybrid model of particle swarm optimization and genetic algorithm for tuning of PID controller parameters for a concentration control of isothermal continuously stirred tank reactor which is generally used to carry out chemical reactions in an industry on a large scale. The time domain study of different methods for tuning of PID controller parameters were observed and it was found that PSO(Particle Swarm Optimization)performance was better than GA(Genetic Algorithm) but the hybrid of GA-PSO gave the best output results in the observation to control the concentration of isothermal continuous stirred tank reactor.