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enormously popular within graduate training programs (Norcross et al., 2002). In addition to standard training in general clinical psychology, these psychologists obtain in-depth training in developmental psychology and child assessment (e.g., behavioral disorders, learning disabilities, and motor developmental delays) and treatment (e.g., play therapy, group therapy, parent consultation). They commonly work in community mental health clinics, child guidance clinics, schools, children s hospitals, and in private practices. Child clinical psychologists may work with children who have experienced physical and/or sexual abuse or who experience attention deficit/hyperactivity disorder, conduct disorders, autism, enuresis (bed wetting), learning disabilities, serious illness, school phobia, posttraumatic stress disorder, or a host of other emotional, behavioral, or medical problems. These psychologists may provide consultation to teachers, school counselors, pediatricians, day care workers, parents, and others. They may assist teachers in classroom behavior management or parents in developing better parenting skills. Pediatric psychologists are child clinical psychologists who generally work with children and families in hospital settings where the child has a significant medical disorder (R. Brown, 2003). These medical problems might include cancer, epilepsy, diabetes, cystic fibrosis, and neurological disorders and disabilities. The pediatric psychologist might offer pain management strategies to a child while helping the family cope more effectively with and locate community resources. He may act as a consultant to various medical units and departments to help physicians, nurses, and others deal with the emotional and behavioral consequences of severe medical illnesses in children. For example, a pediatric psychologist might consult with a physician about an adolescent with diabetes who refuses to monitor his or her blood sugar level due to concerns

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sive therapy). Although there are exceptions, psychiatric training generally focuses on clinical diagnoses and treatment of major psychopathology (i.e., affective or mood disorders, such as bipolar disorder, and psychotic disorders, such as schizophrenia). Training in general human behavior and research is usually minimal. Relative to other mental health disciplines, there are a variety of pros and cons to being a psychiatrist. Advantages include several factors. First, as physicians, psychiatrists have extensive training in the biological basis of behavior and behavioral problems. They are able to use this expertise to understand and treat a wide range of medical and psychiatric problems. Psychiatrists have superior knowledge of medical aspects of certain disorders, and have been trained to take a leadership role vis- -vis these patients. Thus, they can prescribe medication and other biological treatments for their patients, whereas other clinicians must refer patients to an MD if medication or other biological interventions are indicated. However, efforts endorsed by the APA are under way to enable psychologists with specialized training to prescribe medication (Beutler, 2002; Smyer et al., 1993). Second, psychiatrists have a much higher earning potential than any other mental health professional. Starting salaries typically are around $100,000 with average salaries about $150,000 depending on the work setting (Kanapaux, 2003). In comparison, the average salary for practicing psychologists is $80,000 (Kanapaux, 2003). Third, as physicians, psychiatrists generally hold greater status and positions of greater authority, especially in hospital or other medical settings. Higher salaries and prestige are due to the costs and competitiveness of medical education as well as society s admiration of physicians in general. There are several important disadvantages to becoming a psychiatrist. First, the costs

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In this chapter, an online evolving neuro-fuzzy recurrent network (ENFRN) is proposed. The network is capable of perceiving the change in the actual system and adapting (self-organizing) itself to the new situation. Both structure and parameters learning take place at the same time. The network generates a new hidden neuron if the smallest distance between the new data and all the existing hidden neurons (the winner neuron) is more than a prede ned radius. A new pruning algorithm based on the population density is proposed. Population density is the number of times each hidden neuron is used. If the value of the smallest density (the looser neuron) is smaller than a prespeci ed threshold, this neuron is pruned. It is proposed to use a modi ed leastsquares algorithm to train the parameters of the network. The major contributions of this chapter are: (1) The stability of the algorithm of the proposed evolving neuro-fuzzy recurrent network was proven; and (2) the bound of the average identi cation error was found. Three examples are provided to illustrate the effectiveness of the suggested algorithm based on simulations.

Figure 1-4

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